Archive for the ‘Turing Machine’ Category

WETICE 2014 and The Conference Track on the Convergence of Clouds, Grids and Their Management
October 30, 2013

WETICE is an annual IEEE International conference on state-of-the-art research in enabling technologies for collaboration, consisting of a number of cognate conference tracks. The “Convergence of Clouds, Grids and their Management” conference track is devoted to discussing current and emerging trends in virtualization, cloud computing, high performance computing, Grid computing and Cognitive Computing. The tradition that started in WETICE2009 “to analyze current trends in Cloud Computing and identify long-term research themes and facilitate collaboration in future research in the field that will ultimately enable global advancements in the field that are not dictated or driven by the prototypical short term profit driven motives of a particular corporate entity” has resulted in a new computing model that was included in the Turing Centenary Conference proceedings in 2012. The 2013 conference track discussed Virtualization, Cloud Computing and the Emerging Datacenter Complexity Cliff in addition to conventional cloud and grid computing solutions.

The WETICE 2014 conference to be held in Parma, Italy during June, 23rd-25th, 2014, will continue the tradition by continuing the discussions on the convergence of clouds, grids and their management. In addition, it will also solicit papers on new computing models, cognitive computing platforms and strong AI resulting from recent efforts to inject cognition into computing (Turing Machines).

All papers are refereed by the Scientific Review of Committee of each conference track. All accepted papers will be published in the electronic proceedings by the IEEE Computer Society, and submitted to the IEEE digital library. The proceedings will be submitted for indexing through INSPEC, Compendex, and Thomson Reuters, DBLP, Google Scholar and EI Index.

http://wetice.org

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Cellular Resiliency, Turing Machines, New Computing Models and the Zen of Consciousness
June 23, 2013

“WETICE 2012 Convergence of Distributed Clouds, Grids and their Management Conference Track is devoted to transform current labor intensive, software/shelf-ware-heavy, and knowledge-professional-services dependent IT management into self-configuring, self-monitoring, self-protecting, self-healing and self-optimizing distributed workflow implementations with end-to-end resource management by facilitating the development of a Unified Theory of Computing.”

Here is more food for thought…

Abstract:

Cellular biology has evolved to capture dynamic representations of self and its surroundings and a systemic view of monitoring and control of both the self and the surroundings to optimize the organism’s chances of survival. Signaling plays a key role in shaping the structure and behavior of cellular organisms to exhibit a high degree of resiliency by monitoring and controlling its own activity and its interactions with the outside environment with a Zen-like one-ness of the observer and the observed. Evolution has invented the genetic transactions of replication, repair, recombination and reconfiguration to support the survival of living cells by organizing themselves to execute a coordinated set of activities and signaling provides a vehicle for managing the system-wide behavior.

By introducing signaling and self-management in a Turing node and a signaling network as an overlay over the computing network, the current von-Neumann computing model is evolved to bring the architectural resiliency of cellular organisms to computing infrastructure. The new approach introduces the genetic transactions of replication, repair, recombination and reconfiguration to program self-resiliency in distributed computing systems executing a managed workflow. Perhaps, the injection of parallelism and network based composition of “Self” identity are the first steps in introducing the elements of homeostasis and self-management required for developing consciousness in the computing infrastructure.

Introduction:

As recent advances in neuroscience throw new light on the process of evolution of the cellular computing models, it is becoming clear that communication and collaboration mechanisms of distributed computing elements and end-to-end distributed transaction management played a crucial role in the development of self-resiliency, efficiency and scaling which are exhibited by diverse forms of life from the cellular organisms to highly evolved human beings. According to Antonio Damasio (Damasio 2010), managing and safe keeping life is the fundamental premise of biological value and this biological value has influenced the evolution of brain structures. “Life regulation, a dynamic process known as homeostasis for short, begins in unicellular living creatures, such as bacterial cell or a simple amoeba, which do not have a brain but are capable of adaptive behavior. It progresses in individuals whose behavior is managed by simple brains, as in the case with worms, and it continues its march in individuals whose brains generate both behavior and mind (insects and fish being examples)….” Homeostasis is the property of a system that regulates its internal environment and tends to maintain a stable, constant condition of properties like temperature or chemical parameters that are essential to its survival. System-wide homeostasis goals are accomplished through a representation of current state, desired state, a comparison process and control mechanisms.

He goes on to say that “consciousness came into being because of biological value, as a contributor to more effective value management. But consciousness did not invent biological value or the process of valuation. Eventually, in human minds, consciousness revealed biological value and allowed the development of new ways and means of managing it.” The governance of life’s processes is present even in single-celled organisms that lack a brain and it has evolved to the conscious awareness which is the hallmark of highly evolved human behavior. “Deprived of conscious knowledge, deprived of access to the byzantine devices of deliberation available in our brains, the single cell seems to have an attitude: it wants to live out its prescribed genetic allowance. Strange as it may seem, the want, and all that is necessary to implement it, precedes the explicit knowledge and deliberation regarding life conditions, since the cell clearly has neither.  The nucleus and the cytoplasm interact and carry out complex computations aimed at keeping the cell alive.  They deal with the moment-to-moment problems posed by the living conditions and adapt the cell to the situation in a survivable manner. Depending on the environmental conditions, they rearrange the position and distribution of molecules in their interior, and they change the shape of sub-components, such as microtubules, in an astounding display of precision.  They respond under duress and under nice treatment too. Obviously, the cell components carrying out those adaptive adjustments were put into place and instructed by the cell’s genetic material.”  This vivid insight brings to light the cellular computing model that:

  1. Spells out the computational workflow components as a stable sequence of patterns that accomplishes a specific purpose,
  2. Implements a parallel management workflow with another sequence of patterns that assures the successful execution of the system’s purpose (the computing network to assure biological value with management and  safekeeping),
  3. Uses a signaling mechanism that controls the execution of the workflow for gene expression (the regulatory network) and
  4. Assures real-time monitoring and control (homeostasis) to execute genetic transactions of replication, repair, recombination and reconfiguration (Stanier, Moore, 2006).

The managing and safekeeping life efficiently are evident at the lowest level of biological architecture that provides the resiliency that von Neumann was discussing in his Hixon lecture (von Neumann, 1987). ‘‘The basic principle of dealing with malfunctions in nature is to make their effect as unimportant as possible and to apply correctives, if they are necessary at all, at leisure. In our dealings with artificial automata, on the other hand, we require an immediate diagnosis. Therefore, we are trying to arrange the automata in such a manner that errors will become as conspicuous as possible, and intervention and correction follow immediately.’’ Comparing the computing machines and living organisms, he points out that the computing machines are not as fault tolerant as the living organisms. He goes on to say ‘‘It’s very likely that on the basis of philosophy that every error has to be caught, explained, and corrected, a system of the complexity of the living organism would not run for a millisecond.’’

The connection between consciousness and computing models is succinctly summarized by Samad and Cofer (Samad, Cofer, 2001).  While there is no accepted precise definition of the term consciousness, “it is generally held that it is a key to human (and possibly other animal) behavior and to the subjective sense of being human. Consequently, any attempt to design automation systems with humanlike autonomous characteristics requires designing in some elements of consciousness.  In particular, the property of being aware of one’s multiple tasks and goals within a dynamic environment and of adapting behavior accordingly.” They point to two theoretical limitations of formal systems that may inhibit the implementation of computational consciousness and hence limit our ability to design human-like autonomous systems. “First, we know that all digital computing machines are “Turing-equivalent”-They differ in processing speeds, implementation technology, input/output media, etc., but they are all  (given unlimited memory and computing time) capable of exactly the same calculations. More importantly, there are some problems that no digital computer can solve. The best known example is the halting problem; we know that it is impossible to realize a computer program that will take as input another, arbitrary, computer program and determine whether or not the program is guaranteed to always terminate.

Second, by Gödel’s proof, we know that in any mathematical system of at least a minimal power there are truths that cannot be proven. The fact that we humans can demonstrate the incompleteness of a mathematical system has led to the claims that Gödel’s proof does not apply to humans.”

An important implication of Gödel’s incompleteness theorem is that it is not possible to have a finite description with the description itself as the proper part. In other words, it is not possible to read yourself or process yourself as process. In short, Gödel’s theorems prohibit “self-reflection” in Turing machines. Louis Barrett highlights (Barrett, 2011) the difference between Turing Machines implemented using von Neumann architecture and biological systems. “Although the computer analogy built on von Neumann architecture has been useful in a number of ways, and there is also no doubt that work in classic artificial intelligence (or, as it is often known, Good Old Fashioned AI: GOFAI) has had its successes, these have been somewhat limited, at least from our perspective here as students of cognitive evolution.” She argues that the Turing machines based on algorithmic symbolic manipulation using von Neumann architecture, gravitate toward those aspects of cognition, like natural language, formal reasoning, planning, mathematics and playing chess, in which the processing of abstract symbols in a logical fashion and leaves out other aspects of cognition that deal with producing adoptive behavior in a changeable environment. Unlike the approach where perception, cognition and action are clearly separated, she suggests that the dynamic coupling between various elements of the system, where each change in one element continually influences every other element’s direction of change has to be accounted for in any computational model that includes system’s sensory and motor functions along with analysis. To be fair, such couplings in the observed can be modeled and managed using a Turing machine network and the Turing network itself can be managed and controlled by another serial Turing network.  What is not possible is the tight integration of the models of the observer/manager and the observed/managed with a description of the “self” (or a specification of the manager) using parallelism and signaling that are the norm and not an exception in biology.

A more interesting controversy that has erupted regarding the need for new computing models (Wegner, Eberbach, 2004, Cockshott, Michaelson, 2007, Goldin, Wegner, 2008) throws some new light on the need for re-examining the Turing machines, Gödel’s prohibition of self-reflection  and von Neumann’s conjecture. An even more recent discussion of the need for new computing models was presented in the Ubiquity symposium (ACM Ubiquity, 2011). As we describe later, these authors are attempting to address how to model computational problems that cannot be solved by a single Turing machine but can be solved using a set of Turing machines interacting with each other. In particular, the property of being aware of one’s multiple tasks and goals within a dynamic environment and of adapting behavior accordingly which is related to consciousness mentioned earlier is one such problem that a single Turing machine can not solve. The insights into biology suggest that in order to model temporal dynamics of the observer and the observed while also assuring the safe-keeping of the observer (with a “self” identity) requires modifications to the Turing machine to accommodate changes to the behavior while computation is still in progress.

Self, Consciousness, and Emotions – The Dynamic Representation of the Observer and the Observed:

Self-reflection, setting expectations, monitoring the deviations and taking corrective action are essential for managing the business of life through homeostasis and evolution has figured out how to encapsulate the right descriptions to execute the life’s processes using the genetic transaction of replication, repair, recombination and reconfiguration by exploiting parallelism and signaling. As Jonah Lehrer (Lehrer, 2010) describes in his book “How We Decide”, “Dopamine neurons automatically detect the subtle patterns that we would otherwise fail to notice; they assimilate all the data that we can’t consciously comprehend. And, then, once they come up with a set of refined predictions about how the world works, they translate these predictions to emotions.” Emotions, it seems are the instinctual localized component level suggestions for corrective actions based on local experience. Conscience [1] on the other hand, is the adult who correlates the instinctual suggestions with much larger perspective and makes decisions based on global priorities.

It is becoming clear from the recent advances in neuroscience, that self-reflection is a key component in living organisms.  Homeostasis is not possible without a dynamic and active representation of the observer and the observed.

A cellular organism is the simplest form of life that maintains an internal environment that supports its essential biochemical reactions, despite changes in the external environment. Therefore, a selectively permeable plasma membrane surrounding a concentrated aqueous solution of chemicals is a feature of all cells. In addition it is capable of self-replication and self-repair which may be unicellular or multicellular. Unicellular organisms perform all the functions of life. Multicellular organisms contain several different cell types that are specialized to perform specific functions. The cell adapts to its environment by recognition and transduction of a broad range of environmental signals, which in turn activate response mechanisms by regulating the expression of proteins that take part in the corresponding processes. The nucleus of the cell houses deoxyribonucleic acid (DNA) the genetic blueprint of the organism which determines the structure and function of the organism as a whole. The DNA serves two functions. First, it contains instructions for assembling the structural and enzymatic proteins of the cell. Cellular enzymes in turn control the formation of other cellular structures and also determine the functional activity of the cell by regulating the rate at which metabolic reactions proceed. Second, by replicating (making copies of itself), DNA perpetuates the genetic blueprint within all new cells formed within the body and is responsible for passing on genetic information from the survivors to successors.

A gene is a stretch of DNA that contains instructions or code for a particular function such as synthesizing a protein or dictating the assembly of amino acids. A unique set of genes are packaged as chromosomes in complex organisms. A gene regulatory network represents relationships between genes that can be established from measuring how the expression level of each one affects the expression level of the others. In any global cellular network, genes do not interact directly with other genes. Instead, gene induction or repression occurs, the action of specific proteins, which are in turn products of certain genes as well. In essence, gene networks are abstract models that display causal relationships between gene activities and are represented by directed graphs. Nearly all of the cells of a multicellular organism contain same DNA. Yet this same genetic information yields a large number of different cell types. The fundamental difference between a neuron and a liver cell, for example, is which genes are expressed. The regulatory gene network forms a cellular control circuitry defining the overall behavior of the various cells. According to Antonio Damasio (Damasio, 2010), the brain architecture is an evolutionary aid to the business of managing life which consists of managing the body and the management gains precision and efficiency with the presence of circuits of neurons assisting the management. In describing the role of neurons, he says that “neurons are about life and managing life in other cells of the body, and that that aboutness requires two-way signaling. Neurons act on other body cells, via chemical messages or excitation of muscles, but in order to do their job, they need inspiration from the very body they supposed to prompt, so to speak. In simple brains, the body does its prompts simply by signaling to subcortical nuclei. Nuclei are filled with “dispositional know-how,” the sort of knowledge that does not require detailed mapped representations. But in complex brains, the map-making cerebral cortices describe the body and its doings in so much explicit detail that the owners of those brains become capable, for example, of “imaging: the shape of their limbs and their positions in space, or the fact that their elbows hurt or their stomach does”.

The complex network of neural connections and signaling mechanisms collaborate to create a dynamic, active and temporal representation of both the observer and the observed with myriad patterns, associations and constraints among their components. It seems that the business of managing life is more than mere book-keeping that is possible with a Turing machine. It involves the orchestration of an ensemble with a self-identity both at the group and the component level contributing to the system’s biological value. It is a hierarchy of individual components where each node itself is a sub-network with its own identity and purpose which is consistent with the system-wide purpose. To be sure, each component is capable of book-keeping and algorithmic manipulation of symbols. In addition, identity and representations of the observer and the observed at both the component and group level make system-wide self-reflection possible.

In short, the business of managing life is implemented by a system consisting of a network of networks with multiple parallel links that transmit both control information and the mission critical data required to sense and to control the observed by the observer. The data and control networks provide the capabilities to develop an internal representation of both the observer and the observed along with the processes required to implement the business of managing life. The organism is made up of autonomic components making up an ensemble collaborating and coordinating a complex set of life’s processes that are executed to sense and control both the observer and the observed.  In this sense, the brain and the body are part of a collaborating system that has a unique identity and a structure that preserves the interrelationships.  The system consists of:

  1. Components each with a purpose within a larger system (specialization)
  2. All of a component parts must be present for the system to carry out its purpose optimally,
  3. A system’s parts must be arranged in a specific way for the system to carry out its purpose (separation of concerns),
  4. Systems change in response to feedback (collect information, analyze information and control environment using specialized resources), and
  5. Systems maintain their stability (in accomplishing their purpose) by making adjustments based on feedback (homeostasis).

[1] According to Antonio Damasio (Damasio, 2010), consciousness pertains to the knowing of any object or action attributed to a self, while conscience pertains to the good or evil to be found in actions or objects. The identity of self and its safekeeping are essential parts of life processes.  “The non-conscious neural signaling of an individual organism begets the proto-self which permits core self and core consciousness, which allow for an auto-biographical self which permits extended consciousness. At the end of the chain, extended consciousness permits conscience.”

Figure 1 shows the model of core-conscience, its relationship to the Observed and the extended conscience (Damasio, 1999) proposed by Damasio based on his studies in neuroscience.

Figure 1: The mapping of the observer, the observed and myriad models, associations and processes executed using parallel signaling and data exchange networks.  Each component itself is a sub-network with a purpose defined by its own internal models.

Literature is filled with discussion about Gödel’s prohibition of self-reflection in Turing machines and why consciousness cannot emerge from the brain models that depend on Turing machines.  There are many theories on how the human brain is unique and may even involve quantum phenomena or gravity waves (Scott, 1995 and Davis 1992).  However Damasio (Damasio, 2010) takes the evolutionary approach to discuss genomic unconsciousness, the feeling of conscious will, educating the cognitive conscious, the reflective self and its consequences. He goes on to say “in one form or another, the cultural developments manifest the same goal as the form of automated homeostasis.” “They respond to a detection of the imbalance in the life process, and seek to correct it within the constraints of human biology and of the physical and social environment.”

Instead of adding to the already existing controversy (Scott, 1995) on consciousness, we take a different route using Damasio’s emphasis on homeostasis along with the dynamic representation of the observer and the observed. We apply them to extend the Turing machine and its von Neumann Serial computing implementation.  We ask how we can utilize the abstractions that assist in the business of managing life in cellular organisms, discussed above, to enhance the resiliency of distributed computing systems. In the next section we analyze the current implementation of Turing machines and suggest adding some of the abstractions that have proven useful in managing life’s processes to develop a computing model that addresses the problem of being aware of one’s multiple tasks and goals within a dynamic environment and of adapting behavior accordingly.

Turing Machines, Super Turing Machines and DIME Networks:

While a single SPC node lacks self-reflection prohibited by Gödel’s theorems, a network of Turing machines have been successfully used to implement business workflows that observe and manage the external world. This is accomplished by modeling the observed (external to the computing infrastructure) and orchestrating the temporal dynamics of the observed. This has helped us develop complex control systems that can be monitored and controlled with the resiliency of cellular organisms.

However, what is missing is the same resiliency in the infrastructure (or the observer) that implements the control of the observed. Learning fromDamasio’s analysis, in order to introduce consciousness, we must introduce the “self” identity of the observer and the observer’s multiple tasks and goals within a dynamic environment and of adapting behavior accordingly. The “self” specification must include a hierarchy of goals and execution mechanisms to include his concepts of “core” and “extended” selves.

The evolution of computing seems to follow a similar path to cellular organisms in the sense that it emerged as an individual computing element (von Neumann stored program implementation of the Turing machine) and evolved into today’s networks of managed computing elements executing complex workflows that monitor and control external environment.

The Turing machine originally started as a static closed system (Goldin, Wegner, 2008) analogous to a single cell. It was designed for computing algorithms that correspond to mathematical world view. This is the case with Assembler language programming where a CPU is programmed and the Turing machine is implemented using the von Neumann Stored Program Control computing model as shown in figure 2.

Figure 2: A Turing machine with von Neumann Stored Program Control implementation in its simplest form

The Church-Turing thesis stipulates that “Turing machines can compute any effective (partially recursive) functions over naturals (strings). Goldin and Wegner argue that the Church-Turing thesis applies only to effective computations rather than computation by arbitrary physical machines, dynamical systems or humans.

To address this issue, we stipulate that “all computations can be represented as workflows specified by a directed acyclic graph (DAG). Algorithms are a sub set of all computations. An algorithm can be viewed as a workflow of instructions executed by a stored program control (SPC) computing unit (constituting an atomic unit of computation). Then, based on the programming paradigm of one’s choice, one can compose other computing units such as procedures, functions, objects etc., to execute the specified workflow.” This can reconcile the operating system conundrum that states that the operating systems do not terminate as  the Turing machines are required to. As soon as an operating system is introduced, the Turing machine SPC implementation immediately becomes a workflow of computations to implement a process, where each process now behaves as a new Turing machine with SPC implementation. It is as if the operating system is a manager (implementing a management workflow using a group of management Turing machines dedicated for this purpose) controlling a series of other computing Turing machines based on policies set in the operating system. The operating system instructions and the computational flow dependent instructions are mixed to serially execute the process and a sequence of processes. This is analogous to the evolution of multi-cellular organisms where individual cells establish a common management protocol to execute their goals with shared resources. The individual processes may or may not have a common goal but they share the same resources. The operating system communicates with the processes to exert its role using shared memory as shown in Figure 3. While the individual processes do not have fault, configuration, accounting, performance and security management of self, the operating system provides these functions using the signaling abstractions of addressing, alerting, mediation and supervision.

Figure 3: Operating system implements the managed Turing processes.

Since then, multi-threading in a single processor, networked and interactive computing have influenced the computations. In a network, concurrency and influence of one node on another (impact of the environment on the computation) are the new elements that have to be addressed.  The Pi calculus and super Turing models (Eberbach, E., Wegner, P., Goldin, D., 2011) are an attempt to address these aspects. While these attempts are embroiled in controversy, (Cockshott, Michaelson, 2007), what is not in dispute is that a network of computers represents a network of organized Turing machines where each node is a group of Turing machines managed locally. See Figure 4.

Figure 4: A Networked set of Turing machines provide distributed computing services. However this does not provide coordination and management across the two sets of Turing machines.

In such a network, the local operating systems cannot provide Fault, Configuration, accounting, performance and security (FCAPS) management of the system as whole. The disciplines of distributed computing and distributed systems management evolved to address the FCAPS management of the system in an ad-hoc manner without a formal computing model for the system as a whole. This is even more complicated when the system as a whole now acts in unison with a system-wide purpose where one element can influence other elements as pointed out by Louise Barrett (Barrett, 2011).

In this case, the description of the functions performed and the influence of one computation on another has to be encoded at compile time and each computing element does not have the ability to change the behavior at run time. In addition, operating system function is to allocate the resources appropriately to the consumers (processes running applications) and the applications themselves do not have any influence on the resources during run time. For example, if the workload fluctuates, the application has no way of monitoring and controlling the resources.

Figure 5: A network of Turing machines implementing a service workflow that manages the external environment (the observed). The management of the observer is also implemented using the same serial Turing machines where in some nodes the management of the observer and the observed are mixed in serial fashion and some other nodes are exclusively devoted to managing the observer.

If multiple applications are contending for resources, external policies have to be implemented as other Turing machines and the applications themselves are not aware of these external influences. In order to manage distributed set of Turing machines, another set of Turing machines are introduced to provide service management to improve fault, configuration, accounting, performance and security characteristics of the distributed system. See figure 5.

The DIME computing model allows the specification and execution of a recursive composition model where each computing unit at any level specifies and executes the workflow at the lower level. The specification at a higher level eliminates the self-reflection prohibition of Gödel’s theorems on computational units. The parallel implementation of the management workflow and the computational workflow at each level allows the influence of one component in the workflow to influence another component at the lower level. At any level, the computational unit specifies and assures the execution of the lower level workflow thus it becomes the observer observing and controlling the workflow execution at lower level (which is the observed)

This model eliminates the problem of separation of communication between the computing system components in a system and the communication between the computing system and its environment. In current computing models of systems design, treating them as two separate issues has created the current disconnect in the distributed systems theories (Goldin, Wegner, 2007, pp. 22)

Figure 6 shows the new computing model we call distributed Intelligent Managed Element (DIME) network computing model and the resulting computing infrastructure is designed with DIME network architecture.

 

Figure 6: A Distributed Intelligent Managed Element (DIME) with local management of the Turing computing node and signaling channel.  The FCAPS attributes of the Turing node are continuously monitored and controlled based on local policies. In addition the signaling channel allows coordination with global policies.

The DIME network architecture (Mikkilineni 2011) consists of four components:

  1. A DIME node which encapsulates the von Neumann computing element with self-management of FCAPS.
  2. Signaling capability that allows intra-DIME and Inter-DIME communication and control,
  3. An infrastructure that allows implementing distributed service workflows as a set of tasks, arranged or organized in a DAG and executed by a managed network of DIMEs and
  4. An infrastructure that assures DIME network management using the signaling network overlay over the computing workflow

The self-management and task execution (using the DIME component called MICE, the managed intelligent computing element) are performed in parallel using the stored program control computing devices.  The DIME encapsulates the “dispositional know-how.”  Each DIME is programmable to control the MICE and provide continuous supervision of the execution of the programs executed by the MICE. The DIME FCAPS management allows to model and represent dynamic behaviour of each DIME, the state of the MICE and its evolution as a function of time based on both internal and external stimuli. The parallel management architecture allows the observer (a network or subnetworks) that forms a group to monitor and control itself while facilitating the implementation of monitoring and control of the observed in external environment. Parallelism allows dynamic information flow both in the signaling channel and the external I/O channels of the Turing computing nodes.

There are three special features of DNA that contribute to self-resiliency:

  1. Each Turing computing node is controlled by the FCAPS policies set in each DIME. Each read and write are dynamically configurable based on the FCAPS policies.
  2. Each node itself can be a sub-network of DIMES with goals set by the sub-network policies.
  3. The signaling allows dynamic connection management to reconfigure the DIME network thus changing the policies and behaviour.

It is easy to show that the DIME network architecture supports the genetic transactions of replication, repair, recombination and rearrangement.  Figure 7 shows a single node execution of a service in a DIME network.

Figure 7: Single node execution of a DIME

 A single node of a DIME that can execute a workflow by itself or by instantiating a sub-network provides a way to implement a managed DAG  (Directed Acyclic Graph) executing a workflow.  Replication is implemented by executing the same service as shown in figure 8.

DIME Replication

 Figure 8:  DIME Replication

By defining service S2 to execute itself, we replicate S2 DIME.  Note that S2 is a service that can be programmed to terminate instantiating itself further when resources are not available.  In addition, dynamic FCAPS (parallel service monitoring and control) management allows changing the behavior of The ability to execute the control commands in parallel allows dynamic replacement of services during run time.  For example by stopping service S2 and loading and executing service S1, we dynamically change the service during run time.  We can also redirect I/O dynamically during run time. Any DIME can also allow a sub-network instantiation and control as shown in figure 9.  The workflow orchestrator instantiates the worker nodes, monitors heartbeat and performance of workers and implement fault tolerance, recovery, and performance management policies.

Figure 9: Dynamic Service Replication & Reconfiguration

It can also implement accounting and security monitoring and management using the signaling channel.  Redirection of I/O allows dynamic reconfiguration of worker input and output thus providing computational network control.

Figure 10:  Shows DIME Sub-network Implementing Service Composition, Fault & Performance Managements.

Figure 10 shows DIME Sub-network Implementing Service Composition, Fault & Performance Managements. A video link http://youtu.be/Ft_W4yBvrVg provides an animated explanation of the DIME network architecture supporting the genetic transactions of software services implemented using stored program control implementation of the Turing machine.

In summary, the dynamic configuration at DIME node level and the ability to implement at each node, a managed directed acyclic graph using a DIME sub-network provides a powerful paradigm for designing and deploying managed services that are decoupled from the hardware infrastructure management. Figure 11 shows a workflow implementation of monitoring and controlling an external environment (temperature monitoring and fan control to maintain the temperature in a range) using a self-managed DIME network with signaling network overlay.

Figure 11: A workflow implementation using a DIME network. There are two FCAPS management workflows, one managing the observer (computing infrastructure) and the other managing the observed (Thermometer and the Fan)

While the DIME network architecture provides food for thought about Turing, machines, new computing models and the role of the representations of observer and the observed in consciousness, it also has practical utility in developing software exploiting the parallelism and performance of many-core servers (Mikkilineni et. al., 2011). Some of the results demonstrating self-repair, auto-scaling to control the response time of a web server are presented at the Server Design Summit (Mikkilineni, 2011).

Conclusion:

The limitation of Turing Machines as a complete model of computation has been pointed out by (Wegner, Eberbach, 2004). While it was challenged by (Cockshott, Michaelson, 2007), it was rebutted by (Goldin, Wegner, 2008). The main argument for a new computing model was to account for the interactive nature of conventional algorithmic computation and the environment outside the computing element. The Turing model dealing with algorithms is closed and static and does not address the changes affecting the computation from outside while the computation is in progress. In order to account for networked systems in which each change in one element continually influences every other element’s direction of change, more expressive computing model are required. The von Neumann implementation of the Turing machine with its serial processing and mixing of algorithmic computation and interaction using a network of von Neumann computing nodes have given rise to complex management infrastructure that makes it difficult to implement in our IT infrastructure, the architectural resiliency of cellular organisms.

The DIME computing model, by implementing parallel management infrastructure to monitor and control the Turing machine at the atomic level, allows the read and write functions of the conventional Turing machine to be influenced by external interaction. The hierarchical network based (where a node itself can be a sub-network) composition model of DIME network architecture allows the identification of “self” (the observer) at various levels and the representation of the interaction between the observer and the observed.

The beauty of the DIME computing model is that it does not impact the current implementation of the service workflow using von-Neumann SPC nodes (monitoring and control of the observed external systems).  But by introducing parallel control and management of the service workflow, the DIME network architecture provides the required scaling, agility and resilience both at the node level and at the network level (integrating the management and control of self, the observer).  The signaling based network level control of a service workflow that spans across multiple nodes allows the end-to-end connection level quality of service management independent of the hardware infrastructure management systems that do not provide any meaningful visibility or control to the end-to-end service transaction implementation at run time.  The only requirement for the DIME infrastructure provider is to assure that the node OS provides the required services for the service controller to load the Service Regulator and the Service Execution Packages to create and execute the DIME.

The network management of DIME services allows hierarchical scaling using the network composition of sub-networks.  Each DIME with its autonomy on local resources through FCAPS management and its network awareness through signaling can keep its own history to provide negotiated services to other DIMEs thus enabling a collaborative workflow execution.

Each node has a unique identity and supports local behavior and its control using local policies that are programmable using the conventional von Neumann SPC Turing machines. Each sub-network and network allows a group identity (group self) and support group behavior and control.  The resulting network of networks enables system-wide resilient business of managing both the self and the services to monitor and control external behavior. The parallel control network allows dynamic connection management of component functions to create dynamic workflows to accommodate changing environment.

The cellular implementation of the business of managing life may also show us the way to the business of managing our computing infrastructure which has already proven valuable in implementing the business of managing our lives and our environment transcending the body and mind of a single individual. As von Neumann remarked (von Neumann, 1966), “A theorem of Gödel that the next logical step, the description of an object, is one class type higher than the object and is therefore asymptotically longer to describe.” He admitted to twisting the theorem a little while describing the evolution of diversifying computational ecology from simple strings of 0s and 1s (von Neumann, 1987). Perhaps the recursive nature of a network containing sub-networks as nodes along with FCAPS management both at the node and network level, offers the definition of “self-identity” at various levels. While self-reflection at any level is prohibited by Gödel, A higher level “self” provides the required management and control to lower levels. A parallel signaling network, which allows dynamic replication, repair, recombination and reconfiguration, provides a degree of resiliency, efficiency and scaling that are not possible with a network of serial von Neumann implementations of Turing machines only. This may well be a prescription for injecting the property of being aware of one’s multiple tasks and goals within a dynamic environment and of adapting behavior accordingly.

References:

ACM Ubiquity Symposium, (2011) http://ubiquity.acm.org/symposia.cfm

Barrett, L., (2011). Beyond the Brain: How Body and Environment Shape Animal and Human Minds. Princeton, New Jersey: Princeton University Press, p 116, 122

Cockshott, P., Michaelson, G., (2007). Are There New Models of Computation? Reply to Wegner and Eberbach, Computer Journal, vol 50, no, 2, 232-247.

Damasio, A., (1999). The Feeling of What Happens: Body and Emotion in the Making of Consciousness. New York, NY: Harcourt & Company.

Damasio, A. (2010). Self Comes to Mind: Constructing the Conscious Brain. New York: Pantheon Books, p. 25 and p. 35.

Dyson, G. B., (1997). Darwin among the Machines: the evolution of global intelligence. Massachusetts: Helix books, p. 189.

Eberbach, E., Wegner, P., Goldin, D., (2011) Our Thesis: Turing Machines Do Not Model All Computations. (private communication of a unpublished paper)

Goldin, D., Wegner, P., (2008). Refuting the Strong Church-Turing Thesis: the Interactive Nature of Computing, Minds and Machines, 18:1, March, pp.17-38,

Lehrer, J., (2010) How We Decide. Boston, MA: Mariner Books, p. 50

Mikkilineni, R., (2011). Designing a New Class of Distributed Systems. New York,NY: Springer. (http://www.springer.com/computer/information+systems+and+applications/book/978-1-4614-1923-5)

Mikkilineni, R., Morana, G., Zito, D., Di Sano, M., (2011). Service Virtualization using a non-von Neumann Parallel, Distributed & Scalable Computing Model: Fault, Configuration, Accounting, Performance and Security Management of Distributed Transactions, (Preprint)

Mikkilineni, R., (2011). Service Virtualization using a non-von Neumann Computing Model, Server Design Summit (www.serverdesignsummit.com), San Jose, November 29. (A video of the presentation is available at http://www.kawaobjects.com/presentations/ServerDesignSummitVideo.wmv.)

Samad, T., Cofer, T., (2001). Autonomy and Automation: Trends, Technologies, In Gani, R., Jørgensen, S. B., (Ed.) Tools in European Symposium on Computer Aided Process Engineering volume 11, Amsterdam, Netherlands: Elsevier Science B. V., p. 10

Stanier, P., & Moore, G., (2006).  The Relationship Between Genotype and Phenotype: Some Basic Concepts. In Ferretti, P., Copp, A., Tickle, C., & Moore, G., (Ed.), Embryos, Genes and Birth Defects, London: John Wiley, p. 5

Scott, A., (1995). The Controversial New Science of Consciousness: Stairway to the Mind. New York, NY: Copernicus, Springer-Verlag. P.184.

“At the hierarchical level of human conscience it is not possible to report a consensus of the scientific community because there is none. Materialists, functionalists, and dualists are-according to a recent issue of the popular science magazine Omni (October 1993)-engaged in

Slinging mud and hitting low like politicians arguing about tax hikes. Although the epithets are more rarified-here it is “obscuritanist” and “crypto-Cartisian” rather than “liberal” and “right wing”-recent exchanges between neuroscientists and philosophers of mind (and in each group among themselves) feature the same sort of relentless defensiveness and stark opinionated name calling we expect from irate congressmen or trash-talking linebackers.

To the extent that this is a true appraisal of the current status of consciousness, it is unfortunate. Like life, the phenomenon of consciousness is intimately related to several levels of the scientific hierarchy, so the appropriate scientists-cytologists, electrophysiologists, neuroscientists, anesthegiologists, sociologists and ethnologists-should be working together. It is difficult to see how this elusive phenomenon might otherwise be understood.

Davis, P., (1992). The Mind of God: The Scientific Basis for a Rational World. New York, NY: Simon and Schuster.

von Neumann, J., (1966). Theory of Self-Reproducing Automata. Burke, A. W. (Ed.) Chicago, Illinois. University of Illinois Press.

von Neumann, J., (1987). Papers of John von Neumann on Computing and Computing Theory, Hixon Symposium, September 20, 1948, Pasadena, CA, The MIT Press, p454, p457

Wegner, P., Eberbach, E., (2004). New Models of Computation. The Computer Journal, vol 47, No. 1, 4-9.

Is the Software Defined Network (SDN) Another Detour to a Datacenter Dead-end?
August 6, 2012

Introduction

Frustrated by the inability to fiddle with Internet routing in the real world, Stanford computer scientist Nick McKeown and colleagues developed a standard called OpenFlow that essentially opens up the Internet to researchers, allowing them to define data flows using software–a sort of “software-defined networking.” Installing a small piece of OpenFlow firmware (software embedded in hardware) gives engineers access to flow tables, rules that tell switches and routers how to direct network traffic. Yet it protects the proprietary routing instructions that differentiate one company’s hardware from another. SDN is nothing more than the separation of network data traffic processing from the logic and rules controlling the flow, inspection, and modification of that data. Traditional network hardware, i.e. switches and routers, implement these functions in proprietary firmware partitioned respectively into what is known as the data and control planes. While this is a fine research project, as the major vendors start to take this seriously and are attempting to introduce it in the real-world datacenters, one must ask if this will add or reduce complexity in the already complex datacenter where a host of piece meal solutions are offered by mega corporations seeking to continually increase their revenues without an incentive to reduce complexity by eliminating the number of hardware and software components deployed which would cut into their product sales.

Systems theory tells us that as the number of components increase in a system, the cost of complexity could outweigh the benefits unless architectural reorganization provides a way out.  We argue that the management complexity in current IT infrastructure design, based on the serial von Neumann stored program control implementation of the universal Turing machine, is a more fundamental architecture issue related to the lack of resiliency of the computing model than a software design issue. Cockshott et al. (2012) conclude their book “Computation and its limits” with the paragraph “The key property of general-purpose computer is that they are general purpose. We can use them to deterministically model any physical system, of which they are not themselves a part, to an arbitrary degree of accuracy. Their logical limits arise when we try to get them to model a part of the world that includes themselves.” Current generation distributed systems are implemented using a network of Turing machines in which the service and its management are intermixed as shown in figure 1. The resources utilized by the nodes in a network are often controlled by a plethora of management systems which are outside the purview of the service workflow that is utilizing the resources.  Thus the end to end service transaction response is controlled by these management systems which introduce a layer of complexity in coordination and contention resolution making the service much simpler than its management.

Figure 1: Serial von Neumann implementation of Turing Machines

The limitations of the SPC computing architecture were clearly on his mind when von Neumann gave his lecture at the Hixon symposium in 1948 in Pasadena, California (von Neumann, 1987, p. 408). “The basic principle of dealing with malfunctions in nature is to make their effect as unimportant as possible and to apply correctives, if they are necessary at all, at leisure. In our dealings with artificial automata, on the other hand, we require an immediate diagnosis. Therefore, we are trying to arrange the automata in such a manner that errors will become as conspicuous as possible, and intervention and correction follow immediately.” Comparing the computing machines and living organisms, he points out that the computing machines are not as fault tolerant as the living organisms.  He goes on to say “It’s very likely that on the basis of philosophy that every error has to be caught, explained, and corrected, a system of the complexity of the living organism would not run for a millisecond” (von Neumann, 1987,p. 408). It is clear that von Neumann recognized a problem in the way we design computing systems.

“Normally, a literary description of what an automaton is supposed to do is simpler than the complete diagram of the automaton. It is not true a priori that this always will be so. There is a good deal in formal logic which indicates that when an automaton is not very complicated the description of the function of the automaton is simpler than the description of the automaton itself, as long as the automaton is not very complicated, but when you get to high complications, the actual object is much simpler than the literary description.” (von Neumann, 1987,pp. 454-457). He remarked, “It is a theorem of Gödel that the description of an object is one class type higher than the object and is therefore asymptotically infinitely longer to describe.” (von Neumann, 1987,pp. 454-457). The conjecture of von Neumann leads to the fact that “one cannot construct an automaton which will predict the behavior of any arbitrary automaton” (von Neumann, 1987,p. 456). This is so with the Turing machine implemented by the SPC model.

In simpler terms the management complexity is related to the classical Russel Paradox that can be paraphrased as follows: “Who manages the managers?” Gödel’s prohibition of self-reflection in a Turing Machine mandates a hierarchy of Turing machines acting as managers managing other Turing machines implementing the computations described as a sequence of instructions that are compiled into a sequence of 1’s and 0’s. The universal Turing machine (or the general purpose computer) implements these TMs in a synchronous workflow thus prohibiting changes to computations at run-time in any Turing machine while the computation is in progress in that machine (i.e., you cannot change the behavior of that computation (compiled code) till its execution is interrupted).

Current generation server, networking, and storage equipment and their management systems have evolved from server-centric and bandwidth limited network architectures to today’s Cloud computing architecture with virtual servers and broadband networks. During last six decades, many layers of computing abstractions have been introduced to map the execution of complex computational workflows to a sequence of 1s and 0s that eventually get stored in the memory and operated upon by the CPU to achieve the desired result.  These include process definition languages, programming languages, file systems, databases, operating systems etc. While this has helped in automating many business processes, the exponential growth in services in the consumer market also has introduced severe strains on current IT infrastructure. In order to meet the need to rapidly respond to manage the distributed computing resources demanded by changing workloads, business priorities and latency constraints, new layers of resource management are added with the introduction of Hypervisors, virtual machines (VM) and their management. While these layers have made the application or service management more agile, they have introduced a new layer of issues related to their own management. For example, new layers of Virtual machine-level clustering, intrusion detection and performance management, are being introduced in addition to already existing clusters, intrusion detection and performance management systems at the infrastructure, operating systems and distributed resource management layers.

However, this approach is completely unsuited to exploit the new generation many-core servers and high-bandwidth networks now available. The advent of many-core severs with tens and even hundreds of computing cores with high bandwidth communication among them makes the current generation server, networking and storage equipment and their management systems which have evolved from server-centric and bandwidth limited architectures completely unsuited to use in the next generation computing infrastructure efficiently.  It is hard to imagine replicating current TCP/IP-based socket communication, “isolate and fix” diagnostic procedures, and the multiple operating systems (which do not have end-to-end visibility or control of business transactions that span across multiple cores, multiple chips, multiple servers and multiple geographies) inside the next generation many-core servers without addressing their shortcomings.  The many-core servers and processors constitute a network where each node itself is a sub-network with different bandwidths and protocols (socket-based low-bandwidth communication between servers, InfiniBand, or PCI Express bus based communication across processors in the same server and shared memory based low latency communication across the cores inside the processor).

Figure 2 shows the many-core server network supporting multiple bandwidths.

In order to cope with the scaling issues and utilize the hierarchical many-core network of networks effectively, next generation service architecture has to emulate the architectural resiliency of cellular organisms that tolerate faults and implement command and control structures which enable execution of self-configuring, self-monitoring, self-protecting, self-healing and self-optimizing (in short self-*) business processes. This requires new computing models that break the Turing machine barrier to computation by allowing the computer and the computed to be treated in the same model.

Papers Solicited to Address Next Generation Datacenter Infrastructure and Technologies:

The conference on “Convergence of Distributed Clouds, Grids and their Management” sponsored under the Aegis of WETICE 2013 is devoted to addressing next generation computing models which support real-time resource reconfiguration of distributed business workflow execution based on latency constraints, changing workloads and business priorities. It is devoted to addressing the assurance of reliability, availability, performance, account management and security of distributed business process execution with appropriate visibility and control.

The objective of the Conference was first stated in WETICE 2009; “to analyze current trends in Cloud Computing and identify long-term research themes and facilitate collaboration in future research in the field that will ultimately enable global advancements in the field that are not dictated or driven by the prototypical short-term profit driven motives of a particular corporate entity.” We are glad to report that the discussions started in 2009 have directly resulted in an alternative approach to self-managing distributed computing systems totally different from current industry trend showing a way to eliminate the complexity of virtual machines and Hypervisors. If this approach is proven to be theoretically sound (as a paper in WETICE2012 investigated) and extend its usefulness (demonstrated through their feasibility in the form of two proofs of concepts in the last conference) to mission critical environments, the DIME network architecture may yet prove to be an important contribution to computer science.

Following the tradition, the target of the WETICE2013 is to transform current complex, redundant, costly and knowledge intensive IT management into self-configuring, self-monitoring, self-healing and self-optimizing distributed workflow implementations with service management only limited by the speed of light. We identify another emerging area of software defined networks (SDN) as a potential candidate for further investigation without the bias that often surrounds commercial profit motives to see whether the overall complexity of the datacenter will be reduced or the SDNs are yet another layer of complexity.

Papers are solicited to advance the next generation distributed computing and its management infrastructure that leverages the new hardware innovations.  The goals of the conference include (but are not limited to):
  1. Discovering new application scenarios, proposing new operating systems, programming abstractions and tools
  2. Identifying the challenging problem that still need to be solved such as parallel programming, scaling and management of distributed computing elements, and
  3. Reporting results and experiences gained by researchers in building dynamic Grid-based middleware, computing clouds (distributed or otherwise) and workflow management systems.
Submission of papers March 10, 2013
Notification to authors April 1, 2013
Final papers to IEEE-CS April 25, 2013
Paper author’s registration deadline May 10, 2013
 WETICE-2013 Conference June 17-20, 2013

References:

P. Cockshott, L. M. MacKenzie and  G. Michaelson, “Computation and its Limits”, Oxford University Press, Oxford 2012.

J. v.Neumann, Probabilistic logic and the synthesis of reliable organisms from unreliable components, “Automatic studies,” edited by C. E. Shannon, and J. McCarthy, Princeton University Press, 1956, pp. 43-98.

W. Aspray, and A. Burks, “Papers of John von Neumann on Computing and Computer Theory.” Cambridge, MA: MIT Press. 1989.

Cloud Computing, Management Complexity, Self-Organizing Fractal Theory, Non Equilibrium Thermodynamics, DIME networks, and all that Jazz
May 5, 2012

“There are two kinds of creation myths: those where life arises out of the mud, and those where life falls from the sky. In this creation myth, computers arose from the mud and code fell from the sky.”

— George Dyson, “Turing’s Cathedral: The Origins of the Digital Universe”, New York: Random House, 2012.

“The DIME network architecture arose out of the need to manage the ephemeral nature of life in the Digital Universe”

— Rao Mikkilineni (2012)

Abstract:

The explosion of current cloud computing software offerings (both open-sourced and proprietary)  to create public, private and hybrid clouds raises a question. Is it resulting in higher resiliency, efficiency and scaling of service offerings or increasing the complexity by introducing more components in an already crowded datacenter deploying myriad appliances, management frameworks, tools and people, all claiming to help lower total cost of operation? As the reliability, availability, performance, security and efficiency of the total system depends both on the number of components and their configuration, the architecture of a system plays an important role in defining the overall system resiliency, efficiency and scaling. We discuss current cloud computing architecture, the resulting complexity and investigate possible solutions using the self-organizing fractals theory and non-equilibrium thermodynamics. Evolution has taught us that when complexity increases, often, an architectural transformation occurs to lower the overall system entropy. Is a phase transition about to occur in our data centers seeded by the new many-core servers and high bandwidth communications?

Introduction:

According to Holbrook (Holbrook 2003), “Specifically, creativity in all areas seems to follow a sort of dialectic in which some structure (a thesis or configuration) gives way to a departure (an antithesis or deviation) that is followed, in turn, by a reconciliation (a synthesis or integration that becomes the basis for further development of the dialectic). In the case of jazz, the structure would include the melodic contour of a piece, its harmonic pattern, or its meter…. The departure would consist of melodic variations, harmonic substitutions, or rhythmic liberties…. The reconciliation depends on the way that the musical departures or violations of expectations are integrated into an emergent structure that resolves deviation into a new regularity, chaos into a new order, surprise into a new pattern as the performance progresses.” He goes on to explain exquisitely what “all that jazz” means and what it has to do with Dynamic Open Complex Adaptive System or DOCAS.

I borrow the jazz metaphor to understand the current state of affairs in cloud computing. Cloud computing started innocently enough as an attempt to automate systems administration tasks of computing systems to improve the resiliency (availability, reliability, performance and security), efficiency and scaling of services provided by web-hosting data centers. Before the advent of global web e-commerce enabled by broadband networks and ubiquitous access to high-powered computing, the workload fluctuations were not wild-enough to demand very fast response in provisioning to meet them. While enterprise datacenters were not pushed to deal with the wild fluctuations that some web-services companies were, companies such as Amazon, Google, Facebook, Twitter etc., dealing with uncertain (non-deterministic) workload fluctuations took a different approach to improve resiliency and scaling. They took advantage of the increased power in blade servers, high bandwidth networks and virtualization technologies to create virtual machine (VM) based systems administration with multiple VMs in a physical device consolidating workloads that are managed with dynamic resource provisioning. This has become known as cloud computing. Strictly speaking, VM is not essential for automation to improve scaling, auto-failover and live migration of applications and their data; and companies such as Google have chosen their own automation strategies without using VMs. On the other hand, many other enterprises have taken a more conservative approach by not adopting the cloud strategy and avoid the risk of impacting their highly tuned mission critical application availability, performance and security. They are probably correct given the continued occasional outages, security breaches and cost escalation in managing complexity with many public clouds.

Amazon and Google went one step further by offering their flexible infrastructures to developers outside their company to rent the resources with which they could develop, deploy and service their own applications, thus unleashing a new class of developers. Startups could substitute OPEX for CAPEX to obtain the resources required for their new product and services development. Resulting explosion of applications and services has created a new demand for more clouds and more automation of systems administration to extend resiliency and provide a high degree of isolation from multiple tenants sharing resources while resolving the resulting contentions. The result is a complex web of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) offerings to meet the needs of developers, service providers and service consumers.  To be sure, these offerings are not independent. On the contrary, each layer influences the other in a complex set of interactions often in non-deterministic way based on workloads, business priorities and latency constraints. Figure 1 shows an example of these relationships.

Figure 1: Complex relationships of information flow between nested layers and information flows between components in each layer. The complexity is only compounded by multi-vendor offerings in each layer (not shown here)

The origin of complexity is easy to understand. While attempting to solve the issue of multi-tenancy and agility, the introduction of the virtual machines gives rise to another complexity of virtual image management and sprawl control. In order to address VM mobility issue, recent efforts to introduce application level mobility using other container constructs such as Gears, Cartridges etc., in the case of Redhat PaaS (or Dynos in the case of Heroku, the salesforce PaaS), introduce yet another layer of management of Gears and Cartridges (or Dynos). Another example is the Eucalyptus Infrastructure as a Service, which goes to great lengths to provide High Availability (HA) of the Infrastructure platform but fails to guarantee HA of applications. It is left to the applications to fend for themselves.  These ad-hoc approaches to automate management have mushroomed the software required, increased the learning curve and made the operation and maintenance even more complex. While all platforms demonstrate drag and drop software with pretty displays that allow developers to easily create new services, there is no guarantee that if something goes wrong, one will be able to debug and find out where the root cause is. Or there is no assurance that when multiple services and applications are deployed on same platform, the feature interactions and shared resource management provided by a plethora of management systems designed independently will cooperate to provide the required reliability, availability, performance and security at the service level. More importantly, when the services cross server, data-center and geographical boundaries, there is no visibility and control of end to end service connections and their FCAPS management. Obviously, the platform vendors are only very eager to provide professional services and additional software to resolve the issues but without end to end service connection visibility and control that spans across multiple modules, systems, geographies and management systems, troubleshooting expenses could outweigh the realized benefits. What we need probably is not more “code” but an intelligent architecture that results in a synthesis of computing services and their management and a decoupling of end to end service connection and service component management from underlying resource (server, network and storage) management.

Self-organizing Fractals and Non-equilibrium Thermodynamics:

Fortunately, the self-organizing fractal theory (SOFT) and non-equilibrium thermodynamics (NET) (Kurakin 2011), provide a way to analyze complex systems and identify solutions. A very good glimpse into the theory can be found in the video (http://www.scivee.tv/node/4994). According to the SOFT-NET theory, the process of self-organization is scale-invariant and proceeds through sequential organizational state transitions, in a manner characteristic of far-from-equilibrium systems, with macrostructure-processes emerging via phase transition and self-organization of microstructure-processes. Once they have emerged as a result of an organizational transition, newborn structure-processes strive to persist and expand, growing in size/number, diversity, complexity, and order, while feeding on pre-existing energy/matter gradients. Economic competition among alternatively organized structure-processes feeding on the same energy/mater gradients leads to the elimination of economically deficient or inferior structure-processes and the improvement, diversification, and specialization of survivors, who are forced to fill and exploit all the available resource niches (the Darwinian phase of self-organization) (Kurakin 2007). Promoted by mutually profitable exchanges of energy/matter, the self-organization of specializing survivors (structure-processes) into larger scale structure-processes transforms (mostly) competing alternatives into (mostly) cooperating complements. As a result, Darwinian competition is transferred onto a larger spatiotemporal scale, where it commences among alternative organizations of self-organized survivors (the organizational phase (Kurakin 2007). Such an economy-driven, scale-invariant process of self-organization leads to the emergence of increasingly long-lived, multi-scale, hierarchical organizations (structures-processes) that expand over increasingly larger scales of space and time, feeding on available energy/matter gradients and eventually destroying them. Yet because energy/matter exists as a non-equilibrium system of interdependent gradients and conjugated fluxes of interconverting energy/matter forms, new gradients and fluxes are created and become dominant as old gradients and fluxes are consumed and destroyed. Such processes are responsible for the continuous birth, death, and transformation of energy/matter forms.

Obviously, cloud computing systems (or for that matter, distributed computing systems in general based on Turing machines) are not living organisms and thus are not susceptible to self-organization. However, if you substitute information to replace energy/matter, there are many similarities between the structure and dynamics of computing systems and living self-organizing systems. The nested computing layers, meta-stable organizational patterns (both macro- and micro- structures) in each layer, and process evolution through inter-layer interaction are the same features that contribute to self-organization. So one can ask what is missing for the cloud computing environments to become self-organizing. The answer lies in two observations:

  1. First one is the Gödel’s prohibition of self-reflection by computing elements that form the fundamental building block in the computing domain, the Turing machine (TM) (Samad and Cofer, 2001).
  2. Second one is the lack of scale invariant macro and micro structure-processes mentioned above for the organization of computing components and their management across various nested layers resulting from current ad-hoc implementation of computing processes using the serial von Neumann implementation of the Turing machine.

I have discussed both these deficiencies elsewhere (Mikkilineni 2011, 2012). The DIME network architecture proposed there attempts to address both these deficiencies.

The DIME Network Architecture:

In its simplest form a DIME is comprised of a policy manager (determining the fault, configuration, accounting, performance, and security aspects often denoted by FCAPS); a computing element called MICE (Managed Intelligent Computing Element); and two communication channels. The FCAPS elements of the DIME provide setup, monitoring, analysis and reconfiguration based on workload variations, system priorities based on policies and latency constraints. They are interconnected and controlled using a signaling channel which overlays a computing channel that provides I/O connections to the MICE (or the computing element) (Mikkilineni 2011). The DIME computing element acts like a Turing oracle machine introduced in his thesis and circumvents Gödel’s halting and un-decidability issues by separating the computing and its management and pushing the management to a higher level. Figure 2 shows the DIME computing model.

Figure 2: The DIME Computing Model. For details on the different implementations of DIME networks (a LAMP stack without VMs and a native Parallax OS) visit http://www.youtube.com/kawaobjects

In addition the introduction of signaling in the DIME network architecture allows a fractal composition scheme of the DIME network to create a recursive distributed computing engine with scale invariant FCAPS management of the computing workflow at node, sub-network and network level. Figure 2 shows the comparison between living organisms with self-organizing fractal attributes and Cloud computing infrastructure organized to exhibit self-management fractal attributes.

Figure 3: Comparison of the nested hierarchical organization of living organisms and DIME network architecture.

While both models exhibit the genetic transactions of replication, repair, recombination and reconfiguration (Stanier and Moore, 2006) (Mikkilineni 2011), there is a fundamental difference between the two. The DIME network architecture is not self-organizing but it is self-managing based on initial policies and constraints defined at the root levels of the hierarchies. These policies can be modified during run time but only with the influence of agents external to the computing element whose behavior is under modification (at the DIME node, sub-network and network level).

At each level, the FCAPS management defines the initial conditions and policy constraints (meta-model if you will, denoting the context and defining the destiny of the ensuing process workflow) that will define the information flows and workflows executed by the DIME network downstream. The resulting metastable configurations are monitored and managed by the managers upstream. This model exhibits the three-step processes that provide self-management in living organisms – establish routine, monitor cues and respond with corrective action based on FCAPS parameters at every level. Figure 4 shows the metastable configuration entropy of the whole system. The FCAPS parameters monitored provide a measure of system entropy shown and the reconfiguration alters the state from higher entropy to lower entropy providing a “measure” of the stable pattern.

Figure 4: System Entropy as a function of time

The SOFT-NET theories provide a path to reexamine the way we design distributed computing systems. Perhaps the living organisms with their self-organizing properties could provide us a way to bring self-management to cloud computing configurations to improve resiliency, efficiency and scaling. The DIME network architecture is a baby-step to implement a recursive distributed computing engine to execute managed workflows that constitute hierarchical and temporal sequences of events executing business workflows.

The DIME network architecture raises some interesting questions about Turing machines and their management. How is it related to the Universal Turing Machine (UTM)? It is important to point out that I do not claim that DIME networks are the answer to Cloud computing vows or that the UTM can or cannot do what a DIME network does. While communicating Turing machines are modeled by a UTM (Penrose 1989), can the managed Turing machine networks also be modeled by the UTM? Is the scale-invariant organizational macro and micro structure-processes discussed in SOFT-NET theory essential for self-organizing systems? What are the differences between living self-organizing systems and self-managing networks? I leave this to the experts. I only point out that the DIME is inspired by the oracle machine discussed by Turing in his thesis and implements the architectural resiliency of cellular organisms in distributed computing infrastructure by introducing parallel management of both the computing elements and networks. While its feasibility has been demonstrated (Mikkilineni, Morana and Seyler, 2012), the DIME network architecture is still in its infancy and presents an opportunity on the eve of Turing’s centenary celebration to investigate its usefulness and theoretical soundness.  Only time will tell if the DIME network architecture is useful in mission critical environments. Figure 5 shows a comparision of Physical server based computing, Virtual Machine based cloud computing and DIME network implementation in Linux server eliminating the Hypervisors and Virtual Machines.

Figure 5: Comparision between conventional, cloud and DIME network computing paradigms. The DIME network Architecture requires no Hypervisors, Virtual Machines, IaaS or PaaS. Linux processes are FCAPS managed and networked using a middleware library without any changes to the Operating System.

The DIME network architecture with its self-management, parallel signaling network overlay and its recursive distributed computing engine model supports all features that current cloud computing provides and more while eliminating the need for Hypervisors, Virtual Machines, IaaS and PaaS. The DNA offers the simplicity by providing FCAPS management of a Linux process through a middle-ware library using standard services of the Linux operating syatem and parallelism available in a multi-core/many-core processor.

Conclusion:

I conclude with one lesson from the past (Mikkilineni and Sarathy, 2009) I take away working in POTS (Plain Old Telephone System), PANS (Pretty Amazing New Services enabled by the Internet), SANs and Clouds. It is that wherever there is networking, switching always trumps other approaches. When services are executed by a network of distributed components, service switching and end-to-end service connection management are the ultimate meta-stable structure-processes and it seems that cellular organisms, telephone networks, and human network eco-systems have figured this out. Signaling and nested FCAPS management structure-processes seem to be the common ingredients. Therefore, I predict that eventually the data centers which are currently computing resource management centers will transform themselves into services switching centers just as in telephony. Perhaps computer scientists should look to telephony, neuroscience and organizational dynamics for answers than engaging in hackathons and coding ad-hoc complex systems to manage distributed computing resources. SOFT-NET theories seem to be pointing to the right direction. The solution may lie in discovering scale invariant micro- and macro structure processes that provide nested FCAPS management and self-managed local and global policy enforcement. Perhaps Holbrook’s “All that Jazz” metaphor is an appropriate metaphor for cloud computing research. Time may be ripe for the reconciliation (the synthesis of the thesis of implementing services and the anti-thesis of services management).

References:

Holbrook, Morris B. 2003. ” Adventures in Complexity: An Essay on Dynamic Open Complex Adaptive Systems, Butterfly Effects, Self-Organizing Order, Coevolution, the Ecological Perspective, Fitness Landscapes, Market Spaces, Emergent Beauty at the Edge of Chaos, and All That Jazz.” Academy of Marketing Science Review [Online] 2003 (6) Available: http://www.amsreview.org/articles/holbrook06-2003.pdf

Kurakin, A., Theoretical Biology and Medical Modelling, 2011, 8:4. http://www.tbiomed.com/content/8/1/4

Kurakin A: The universal principles of self-organization and the unity of Nature and knowledge. 2007 [http://www.alexeikurakin.org/text/thesoft.pdf ].

Mikkilineni, R., Sarathy, V., (2009), “Cloud Computing and the Lessons from the Past,” Enabling Technologies: Infrastructures for Collaborative Enterprises, 2009. WETICE ’09. 18th IEEE International Workshops on , vol., no., pp.57-62, June 29 2009-July 1 2009. doi: 10.1109/WETICE.2009.

Mikkilineni, R., (2011). Designing a New Class of Distributed Systems. New York,NY: Springer. (http://www.springer.com/computer/information+systems+and+applications/book/978-1-4614-1923-5)

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Path to Self-managing Services: A Case for Deploying Managed Intelligent Services Using Dumb Infrastructure in a Stupid Network
February 2, 2012

“WETICE 2012 Convergence of Distributed Clouds, Grids and their Management Conference Track is devoted to transform current labor intensive, software/shelf-ware-heavy, and knowledge-professional-services dependent IT management into self-configuring, self-monitoring, self-protecting, self-healing and self-optimizing distributed workflow implementations with end-to-end service management by facilitating the development of a Unified Theory of Computing.”

“In recent history, the basis of telephone company value has been the sharing of scarce resources — wires, switches, etc. – to create premium-priced services. Over the last few years, glass fibers have gotten clearer, lasers are faster and cheaper, and processors have become many orders of magnitude more capable and available. In other words, the scarcity assumption has disappeared, which poses a challenge to the telcos’ “Intelligent Network” model. A new type of open, flexible communications infrastructure, the “Stupid Network,” is poised to deliver increased user control, more innovation, and greater value.”

                     —–Isenberg, D. S., (1998). “The dawn of the stupid network”. ACM netWorker 2, 1, 24-31.

Much has changed since the late 90’s that drove the Telco’s to essentially abandon their drive for supremacy in intelligent services creation, delivery and assurance business and take the back seat in the information services market to manage the ‘stupid network’ that merely carries the information services.  You have to only look at the demise of major R&D companies such as AT&T Bell Labs, Lucent, Nortel, Alcatel and the rise of a new generation of services platforms from Apple, Amazon, Google, Facebook, Twitter, Oracle and Microsoft to notice the sea change that has occurred in a short span of time. The data center has replaced the central office to become the hub from which myriad voice, video and data services are created, and delivered on a global scale. However the management of these services which determines their resiliency, efficiency and scaling is another matter.

While, the data center value has been the sharing of expensive resources – processor speed, memory, network bandwidth, storage capacity, throughput and IOPs – to create premium-priced services, over the last couple of decades, the complexity of the infrastructure and its management has exploded. It is estimated that up to 70% of the total IT budget now goes to the management of infrastructure rather than to develop new services (www.serverdesignsummit.com). It is important to define what TCO (total cost of ownership) we are talking about here because it is often, used to justify different solutions as the following picture showing three different TCO representations of a data center. Figure 1 shows three different TCO views presented by three different speakers in the Server Design Summit in November 2011.  Each graph, while it is accurate, represents a different view. For example, the first view represents the server infrastructure and its management cost. The second one represents the power infrastructure and its management. The third view shows both the server infrastructure and power management. As you can see the total power and its management, while steadily increasing, is only a small fraction of the total infrastructure management cost.  In addition, these views do not even show the network and storage infrastructure and their management. It is also interesting to see the explosion of management cost shown in figure 3 over the last two decades. Automation has certainly improved the number of servers that can be managed by a single person by orders of magnitude. This is borne by the labor cost in the left picture by Intel which shows it is about 13% of the TCO from server view-point. But this does not tell the whole story.

Figure 1: Three different views of Data center TCO presented in the Server Design Summit conference in November 2011 (http://www.serverdesignsummit.com/English/Conference/Proceedings_Chrono.html). These views do not touch the storage, network and application/service management costs both in terms of software systems and labor.

A more revealing picture can be obtained by using the TCO calculator by one of the Virtualization infrastructure vendors. Figure 2 shows percentage Total Cost of Ownership (TCO) (for a 1500 server data center) over five years by each component with and without virtualization.

Figure 2: Five Year TCO of Virtualization According to a Vendor ROI Calculator. While virtualization reduces the TCO from 35% to 25%, it is almost offset by the software, services and training costs.

While virtualization introduces many benefits such as consolidation, multi-tenancy in a physical server, real-time business continuity and elastic scaling of resources to meet wildly fluctuating workloads, it adds another layer of management systems in addition to current computing, network, storage and application management systems. Figure 3 shows a reduction by 50% of the five-year TCO with virtualization. The Virtual Machine density of about 13 allows a great saving in hardware costs which is somewhat off-set by the new software, training and services costs of virtualization.

Figure 3: TCO over 5 Years with virtualization of 1500 servers using 13 VMs per Server. While the infrastructure and administration costs drop, it is almost offset by the software, services and training costs.

In addition, there is the cost of new complexity in optimizing the 13 or so VMs within each server in order to match the resources (network bandwidth, storage capacity, IOPs and throughput) to application workload characteristics, business priorities and latency constraints. According to a storage consultant, Jon Toigo “Consumers need to drive vendors to deliver what they really need, and not what the vendors want to sell them. They need to break with the old ways of architecting storage infrastructure and of purchasing the wrong gear to store their bits: Deploying a “SAN” populated with lots of stovepipe arrays and fabric switches that deliver less than 15% of optimal efficiency per port is a waste of money that bodes ill for companies in the areas of compliance, continuity, and green IT.”

Resource management based data center operations miss an important feature of services/applications management which is that all services are not created equal. They have different latency and throughput requirements. They have different business priorities and different workload characteristics and fluctuations. What works for the goose does not work for the gander. Figure 4 shows a classification of different services based on their throughput and latency requirements presented by Dell in the server design summit. The applications are characterized by their need for throughput, latency and storage capacity. In order to take advantage of the differing priorities and characteristics of the applications, additional layers of services management are introduced which focus on service specific resource management. Various appliance or software based solutions are added to the already complex resource management suites that address server, network and storage to provide service specific optimization. While this approach is well suited for making recurring revenues for vendors, it is not ideally suited for customers to lower the final TCO when all piece-wise TCO’s are added up. Over a period of time, most of these appliances and software end up as shelf-ware while the venodors tout more new TCO reducing solutions. For example, a well known solution vendor makes more annual revenue from maintenance and upgrades than new products or services that help their cutomers really reduce the TCO.

 Figure 4: Various services/Applications characterized by their throughput and latency requirements. Current resource management based data center does not optimally exploit the resources based on application/service priority, workload variations and latency constraints. It is easy to see the inefficiency in deploying a “one size fits all” infrastructure. It will be more eff icient to tailor “dumb” infrastructure and “Stupid Network” pools specialized to cater to different latency and throughput characteristics and let intelligent services provision themselves with the right resources based on their own business priorities, workload characteristics and latency constraints. This requires the visibility and control of service specification, management and execution available at run time which necessitates a search for new computing models.

In addition to the current complexity and cost of resource management to assure service availability, reliability, performance and security, there is even more fundamental issue that plagues the current distributed systems architecture. A distributed transaction that spans multiple servers, networks and storage devices in multiple geographies uses resources that span across multiple data centers. The fault, configuration, accounting, performance and security (FCAPS) of a distributed transaction behavior requires the end-to-end connection management more like telecommunication service spanning distributed resources. Therefore, focusing on only resource management in a data center without the visibility and control of all resources participating in the transaction will not provide assurance of service availability, reliability, performance and security.

Distributed transactions transcend the current stored program control implementation of the Turing machine which is at the heart of the atomic computing element in current computing infrastructure.  The communication and control are not an integral part of this atomic computing unit in the stored program control implementation of the Turing machine. The distributed transactions require interaction which integrates computing, control and communication to provide the ability to specify and execute highly temporal and hierarchical event flows. According to Goldin and Wegner, Interactive computation is inherently concurrent, where the computation of interacting agents or processes proceeds in parallel. Hoare, Milner and other founders of concurrency theory have long realized that Turing Machines (TM) do not model all of computation (Wegner and Goldin, 2003). However, when their theory of concurrent systems was first developed in the late ’70s, it was premature to openly challenge TMs as a complete model of computation. Their theory positions interaction as orthogonal to computation, rather than a part of it. By separating interaction from computation, the question whether the models for CCS and the Pi-calculus went beyond Turing Machines and algorithms was avoided. The resulting divide between the theory of computation and concurrency theory runs very deep. The theory of computation views computation as a closed-box transformation of inputs to outputs, completely captured by Turing Machines. By contrast, concurrency theory focuses on the communication aspect of computing systems, which is not captured by Turing Machines – referring both to the communication between computing components in a system, and the communication between the computing system and its environment. As a result of this division of labor, there has been little in common between these fields and their communities of researchers. According to Papadimitriou (Papadimitriou, 1995), such a disconnect within the theory community is a sign of a crisis and a need for a Kuhnian paradigm shift in our discipline.”

Kuhnian paradigm shift or not, a new computing model called DIME computing model (discussed in WETICE2010) provides a convergence of these two disciplines by addressing the computing and the communications in a single computing entity that is a managed Turing machine. The DIME network architecture provides a fractal (recursive) composition scheme to create an FCAPS managed network of DIMEs implementing business workflows as DAGs supporting both hierarchical and temporal event flows. The DIME computing model supports only those computations that can be specified as managed DAGs where a management signaling network overlay allows execution of managed computing tasks (executed by a computing unit called MICE) in each Turing machine node that is endowed with self-management using parallel computing threads. The MICE (see the video referenced in this blog for a description of DIME and its use in distributed computing and its management) constitutes the atomic Turing machine that is controlled by the FCAPS manager in a DIME which allows configuring, executing and managing the MICE to load and execute well specified computing workflow and its FCAPS management. The MICE under parallel real-time control of the DIME FCAPS manager aided by a signaling network overlay provides control over start, stop, read and write abstractions of the Turing machine. Two implementations have proven the existence proof for the DIME network architecture.

Figure 5 shows a DIME network implementing Linux, Apache, MySQL and PHP/Perl/Python web services delivery and assurance infrastructure.

Figure 5: The GUI showing the configuration of a LAMP Cloud (Mikkilineni, Morana, Zito, Di Sano, 2012). Each Apache and DNS are DIME aware running in a DIME aware Linux Operating System which, transforms a process into a managed element in the DIME network. A video describes the implementation of auto-failover, auto-scaling and performance management of the DIME aware LAMP cloud

Look Ma! No Hypervisor or VM in My Cloud (See Video)

The prototype implementations demonstrates a side effect of the DIME network architecture, which combines the computing and communication abstractions at an atomic level, – it decouples the services management from the underlying hardware infrastructure management. This makes it possible to implement highly resilient distributed transactions with auto-scaling, self-repair, state-aware migration, and self-protection – in-short, end-to-end transaction FCAPS management – based on business priorities, workload fluctuations and latency constraints.  No Hypervisors or VMs are required. The intelligent management of services workflow with resilient distributed transactions offers a new architecture for the data center infrastructure. For the first time it will be possible to remove embedding service management in the infrastructure management intelligence using myriad expensive appliances and software systems. It will be possible to design new tiers of dumb infrastructure pools (of servers, storage and network devices) with different latency and throughput characteristics and the services will be able to manage themselves based on policies by requesting appropriate resources based on their specifications. They will be able to self-migrate when quality of service levels are not met. The case for dumb infrastructure on a stupid network with intelligent services management puts forth the following advantages:

  1. Separation of concerns: The network, storage and server hardware provides hardware infrastructure management with signaling enabled FCAPS management. They do not encapsulate service management as the current generation equipment does.
  2. Specialization: The hardware is designed to meet specific latency and throughput characteristics to simplify its design through specialization. Different hardware with FCAPS management and signaling will provide plug and play components at run time.
  3. End-to-end service connection FCAPS management using the signaling network overlay allows dynamic service FCAPS management facilitating self-repair, auto-scaling, self-protection, state-aware migration and end to end transaction security assurance.

Figure 4 shows an example design of a possible storage device using simple storage architecture enabled with FCAPS management over a signaling overlay. It can be easily built with commercially off the shelf (COTS) hardware. This design allows separation of the services management from storage device management and eliminates a host of storage software management systems thus simplifying the data center infrastructure.

Figure 5: A gedanken design of autonomic storage and autonomic storage service deployment using the new DIME network architecture. The signaling overlay and FCAPS management are used to provide dynamic service management. Each service can request, using standard Linux OS services during run time, services from the storage device based on business priorities, workload fluctuations and latency constraints.

It is easy to see that the service connection model eliminates the need for clustering and provides new ways to provide transaction resilience with features such as service call forwarding, service call waiting, data broadcast, 800 service call model etc. It is also equally easy to see that with many-core servers, how the DIME Network architecture eliminates the inefficiencies of communication between Linux images within the same container (e.g., TCP/IP) and also how simple SAS storage and Flash storage can replace current generation appliance based storage strategies and their myraid management systems. Looking at the trends, it is easy to see that a paradigm shift soon will be in play to transform the data centers from their current role of being just managed server, networking, and storage hosting centers (whether physical or virtual), to true service switching centers with telecom grade trust. The emphasis will shift from resource switching and resource connection management to services switching and service connection management thus replacing the current efforts to replicate the complexity inside the data center today, also inside the many-core servers. With the resulting decoupling of services management from the infrastructure management, the next generation data centers will perhaps be more like central offices of the old Telcos, switching service connections.

Obviously the new computing model is in its infancy and requires participation from academicians who can validate or reject its theoretical foundation, VCs who can see beyond current approaches and are not satisfied by how many servers can be managed by a single administrator to measure the data center efficiency (as one Silicon Valley VC claimed it as progress in the Server Design Summit) and architects who exploit new paradigms to disrupt the status-quo. The DIME computing model by allowing Linux processes to be converted into a DIME network transcending physical boundaries allows easy migration from current infrastructure to the new one without abandoning legacy applications as the prototype of LAMP cloud demonstrates.

In closing, I like to point out that there have been many calls for a new computing model that combines computing and communication at an atomic computing element level which the Turing machine falls short as discussed above. However, without high bandwidth communication and exploitation of the parallelism that is abundant in the new generation hardware, it is not practically very useful to seriously utilize such new computing models. However, it seems that the hardware advances have outpaced the software advances and perhaps it is about time for computer scientists to seriously take a second look at addressing the software short-fall in dealing with distributed transactions. As the following fable illustrates, it may be futile to look for parallel break-through solutions in a serial boat.

“When Master Foo and his student Nubi journeyed among the sacred sites, it was the Master’s custom in the evenings to offer public instruction to UNIX neophytes of the towns and villages in which they stopped for the night.  On one such occasion, a methodologist was among those who gathered to listen.  “If you do not repeatedly profile your code for hot spots while tuning, you will be like a fisherman who casts his net in an empty lake,” said Master Foo.
“Is it not, then, also true,” said the methodology consultant, “that if you do not continually measure your productivity while managing resources, you will be like a fisherman who casts his net in an empty lake?”
“I once came upon a fisherman who just at that moment let his net fall in the lake on which his boat was floating,” said Master Foo. “He scrabbled around in the bottom of his boat for quite a while looking for it.”  “But,” said the methodologist, “if he had dropped his net in the lake, why was he looking in the boat?”  “Because he could not swim,” replied Master Foo.
Upon hearing this, the methodologist was enlightened”        — Master Foo and the Methodologist
                                                                   (http://www.catb.org/esr/writings/unix-koans/methodology-consultant.html)

If you have transformational research results, or want to make a real difference in computer science research, see Call for Papers at:

www.workshop.kawaobjects.com and http://WETICE.org