Changing Landscape of Backup and Disaster Recovery

“Consumers need to drive vendors to deliver what they really need, and not what the vendors want to sell them.”

——  Jon Toigo (http://www.datastorageconnection.com/doc.mvc/Jon-Toigo-Exposes-More-About-Data-Storage-Ven-0001 )

Starting from the mainframe datacenters where applications are accessed using narrow bandwidth networks and dumb terminals and evolving to client-server and peer-to-peer distributed computing architectures which exploit higher bandwidth connections, business process automation has contributed significantly to reduce the TCO. With the Internet, global e-commerce was enabled and the resulting growth in commerce led to an explosion of storage.  Storage networking and resulting NAS (network attached storage) and SAN (storage area network) technologies have further changed the dynamics of the enterprise IT infrastructure in a significant way to meet business process automation needs.  The storage backup and recovery technologies have further improved the resiliency of services delivery processes by improving the time it takes to respond in case of service failure.  Figure 1 shows the evolution of the data recovery time objective, (the recovery point objective (RPO) is the point in time to which you must recover data as dictated by business needs.  Recovery time objective (RTO) is the period of time after an outage in which the application and its data must be restored to a predetermined state defined by RPO.), which dropped from days to minutes and seconds.  While the productivity, flexibility and global connectivity made possible with this evolution have radically transformed the business economics of information systems, the complexity of heterogeneous and multi-vendor solutions have created high dependence on specialized training and service expertise to assure availability, reliability, performance and security of various business applications.

Figure 1: The evolution of Recovery Time Objective. Virtualization of server technology provides an order of magnitude improvement in the way applications are backed-up, recovered and protected against disasters.

Successful implementation must integrate various server, network and storage centric products with their local optimization best-practices with end-to-end optimization strategies.  While each vendor attempts to assure their success with more software and services, the small and medium enterprises often cannot afford the escalating software and service expenses associated with optimization strategies and become vulnerable.  The exponential growth in services demand for voice, data and video in the consumer market also has introduced severe strains on current IT infrastructures.  There are three main issues that are currently driving distributed computing solutions to seek new approaches:

  1. Current IT datacenters have evolved to meet the business services needs in an evolutionary fashion from server-centric application design to client-server networking to storage area networking without an end-to-end optimized architectural transformation along the way.  The server, network and storage vendors optimized management in their own local domains often duplicating functions from other domains to compete in the market place.  For example, cache memory is used to improve the performance of service transactions by improving response time. However, redundancy of cache management in server, storage and even network switches make tuning of the response time a complex task requiring multiple management systems. Application developers have also started to introduce server, storage and network management within their applications.  For example, Oracle is not just a database application.  It also is a storage manager, and a network manager as well as being an application manager.  It tries to optimize all its resources for performance tuning.  No wonder it takes an army of experts to keep it going.  The result is an over-provisioned datacenter with multiple functions duplicated many times by the server, storage and networking vendors.  Large enterprises with big profit margins throw human bodies, tons of hardware and a host of custom software and shelf-ware packages to address their needs.  Some data centre managers do not even know what assets they have — of course, yet another opportunity for vendors to sell an asset management system to discover what is available, and services to provide asset management using such an asset manager.  Another system is de-duplication software that finds out multiple copies of the same files and removes duplication.  This shows how expensive it is to clean up after the fact.
  2. Heterogeneous technologies from multiple vendors that are supposed to reduce IT costs actually increase the complexity and management costs.  Today, many CFOs consider IT as a black hole that sucks in, expensive human consultants and continually demands capital and operational expenses to add hardware and software which often end up as shelf-ware because of their complexity.  Even for mission-critical business services, enterprises CFOs are starting to question the productivity and effectiveness of current IT infrastructures.  It becomes even more difficult to justify the costs and complexity to support the massive scalability and wild fluctuations in workloads demanded by consumer services.  The price point is set low for the mass market but the demand is high for massive scalability (a relatively simple, but massive, service like Facebook is estimated to use about 40,000 servers and Google is estimated to run a million servers to support its business).
  3. More importantly, Internet-based consumer services such as social networking, e-mail and video streaming applications have introduced new elements: wild fluctuations in demand, massive scale of delivery to a divergent set of customers.  The result is an increased sensitivity to the economics of service creation, delivery and assurance. Unless the cost structure of IT management infrastructure is addressed, the mass-market needs cannot be met profitably.  Large service providers such as Amazon, Google, Facebook etc., have understandably implemented alternatives to meet wildly fluctuating workloads, massive scaling of customers and latency. constraints to meet demanding response time requirements.

Cloud computing technology has evolved to meet the needs of massive scaling, wild fluctuations in consumer demand and response time control of distributed transactions spanning multiple systems, players and geographies.  More importantly, cloud computing changes the backup and Disaster Recovery (DR) strategies in a drastic manner reducing the RTO to minutes and seconds doing much better than SAN/NAS based server-less backup and recovery strategies. Live migration is accomplished as follows:

  1. The entire state of a virtual machine is encapsulated by a set of files stored on shared storage such as Fibre Channel or iSCSI Storage Area Network (SAN) or Network Attached Storage (NAS).
  2. The active memory and precise execution state of the virtual machine is rapidly transferred over a high-speed network, allowing the virtual machine to instantaneously switch from running on the source host to the destination host. This entire process could take less than few seconds on a Gigabit Ethernet network.
  3. The networks being used by the virtual machine are virtualized by the underlying host. This ensures that even after the migration, the virtual machine network identity and network connections are preserved.

While Virtual machines improve resiliency and live migration to reduce the RTO, the increased complexity of hypervisors, their orchestration, Virtual Machine images and their management adds an additional burden in the datacenter. Figure 2 shows the evolution of current datacenters from the mainframe days to the cloud computing transformation.  The cost of creating and delivering a service has continuously decreased with increased performance of hardware and software technologies. What used to take months and years to develop and deliver new services now only takes weeks and hours. On the other hand, as service demand increased with ubiquitous access using the Internet and broadband networks, the need for resiliency (availability, reliability, performance and security management), efficiency and scaling also put new demands on service assurance and hence on the need for continuous reduction of RTO and RPO. The introduction of SAN server-less backup and virtual machine migration in turn have increased complexity and hence the cost of managing the service transactions during delivery while reducing the RTO and RPO.

Figure 2: Cost of Service Creation, Delivery and Assurance with the Evolution of Datacenter Technologies. The management cost has exploded because of a myriad point-solution appliances, software and shelf-ware are cobbled together from multiple vendors. Any future solution that addresses the datacenter management conundrum must provide end-to-end service visibility and control transcending multiple service provider resource management systems. Future datacenter focus will be on a transformation from Resources Management to Services Switching to provide telecom-grade “trust”.

The increased complexity of management of services implemented using the von Neumann serial computing model executing a Turing machine turns out to be more a fundamental architectural issue related to Godel’s prohibition of self-reflection in Turing machines than a software design issue. Cockshott et al. 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.” While the last statement is not strictly correct (for example current operating systems facilitate incorporating computing resources and their management interspersed with the computations that attempt to model any physical system to be executed in a Turing machine), it still points to a fundamental limitation of current Turing machine implementations of computations using the serial von Neumann stored program control computing model. The universal Turing machine allows a sequence of connected Turing machines synchronously model a physical system as a description specified by a third-party (the modeler). The context, constraints, communication abstractions and control of various aspects during the execution of the model (which specifies the relationship between the computer acting as the observer and the computed acting as the observed) cannot be also included in the same description of the model because of Gödel’s theorems of incompleteness and decidability. Figure 3 shows the evolution of computing from mainframe/client-server computing where the management was labor-intensive to the cloud computing paradigm where the management services (which include the computers themselves in the model controlling the physical world) are automated.

 Figure 3: Evolution of Computing with respect to Resiliency, Efficiency and Scaling.

The first phase (of conventional computing) depended on manual operations and served well when the service transaction times and service management times could be very far apart and did not affect the service response times. As the service demands increased, service management automation helped reduce the gap between the two transaction times at the expense of increased complexity and resulting cost of management. It is estimated that 70% of today’s IT budget goes to self-maintenance and only 30% goes to new service development. Figure 4 shows current layers of systems contributing to cloud management.

Figure 4: Services and their management complexity

The origin of complexity is easy to understand. Current ad-hoc distributed service management practices originated from server-centric operating systems and narrow bandwidth connections. The need to address end-to-end service transaction management and the resource allocation and contention resolution required to address changing circumstances which, depend on business priorities, latency and workload fluctuations, were accommodated as an after-thought. In addition, open competitive market place has driven server-centric, network-centric and storage-centric oriented devices and appliances to multiply. The resulting duplication of many of the management functions in multiple devices without an end-to-end architectural view has largely contributed the cost and complexity of management. For example the storage volume management is duplicated in server, network and storage devices leading to a complex web of performance optimization strategies. Special purpose appliance solutions have sprouted to provide application, network, storage, and server security often duplicating many of the functions. Lack of an end-to-end architectural framework has led to point solutions that have dominated service management landscape often negating the efficiency improvements of service development and delivery made possible by the hardware performance improvements (Moore’s law) and software technologies and development frameworks.

The escape from this conundrum is to re-examine the computation models and circumvent the computational limit to go beyond Turing machines and serial von-Neumann computing model. Recently proposed computing model implemented in the DIME network architecture (Designing a New Class of Distributed Systems, Springer 2011) attempts to provide a new approach based on the old Turing O-machine proposed by Turing in his thesis. The phase 3 in figure 3 shows the new computing model implementing non-von Neumann managed Turing machine to implement hierarchical self-management of temporal computing processes. The implementation exploits the parallel threads and high bandwidth available with many-core processors and provides auto-scaling, live-migration, performance optimization and end to end transaction security by providing FCAPS (fault, configuration, accounting, performance and security) management of each Linux process and a network of such Linux processes provide a distributed service transaction. This eliminates the need for Hypervisors and Virtual machines and their management while reducing complexity. Since a Linux process is virtualized instead of a Virtual machine, the backup and DR are at a process level and also include a network of processes providing the service. Hence it is much more light-weight than VM based backup and DR.

In its simplest form the DIME computing model modifies the Turing machine SPC implementation by exploiting the parallelism and high bandwidth available in today’s infrastructure.

Figure 5: The DIME Computing Model – A Managed Turing Machine with Signaling incorporates the spirit of Turing Oracle machine proposed in his thesis.

Figure 5 shows the transition from the TM to a managed TM by incorporating three attributes:

  1. Before any read or write, the computing element checks the fault, configuration, accounting, performance and security (FCAPS) policies assigned to it,
  2. Self-management of the computing element is endowed by introducing parallel FCAPS management that sets the FCAPS policies that the computing element obeys, and
  3. An overlay of signaling network provides an FCAPS monitoring and control channel which allows the composition of managed network of TMs implementing managed workflows.

Figure 6 shows the services architecture with DIME network management providing end-to-end service FCAPS management.

Figure 6: Service Management with DIME Networks

The resulting decoupling of services management from infrastructure management provides a new approach to service management including backup and DR. While, the DIME computing model is in its infancy, two prototypes have already demonstrated its usefulness one with a LAMP stack and another with a new native-OS designed for many-core servers. Unlike Virtual Machine based backup and DR, the DIME network architecture supports auto-provisioning, auto-scaling, self-repair, live-migration, secure service isolation, and end-to-end distributed transaction security across multiple devices at the process level in an operating system. Therefore, this approach not only avoids the complexity of Hypervisors and Virtual machines (although, it still works with Virtual servers) but also allows adopting live-migration to existing applications without requiring changes to their code. In addition, it offers a new approach where the hardware infrastructure is simpler without the burden of anticipating service level requirements and let intelligence of services management reside in the services infrastructure leading to the deployment of intelligent self-managing services using a dumb infrastructure on stupid networks.

In conclusion, we emphasize that the DIME network architecture works with or without Hypervisors and associated Virtual Machine, IaaS and PaaS complexity and allows uniform service assurance across hybrid clouds independent of the service provider management systems. Only the Virtual server provisioning commands are required to configure just enough OS, DIMEX libraries and execute service components using DNA.

The power of DIME network architecture is easy to understand. By introducing parallel management to the Turing machine, we are converting a computing element to a managed computing element. In current operating systems, it is at the process level. In the new native operating system (parallax-OS) we have demonstrated, it is the Core in a many-core processor. A managed element provides plug-in dynamism to service architecture.

Figure 7 shows a service deployment in a Hybrid cloud with integrated service assurance across the private and public clouds without using service provider management infrastructure. Only the local operating system is utilized in DIME service network management.

Figure 7: A DNA based services deployment and assurance in a Hybrid Cloud. The decoupling of dynamic service provisioning and management from infrastructure resource provisioning and management (server, network and storage administration) enabled by DNA makes static provisioning of resource pools possible and dynamic service migration of services allows them to seek right resources at the right time based on workloads, business priorities and latency constraints.

As mentioned earlier, the DIME network architecture is still in its infancy and researchers are developing both the theory and practice to validate its usefulness in mission critical environments. Hopefully in this year of Turing centenary celebration, some new approaches will address the computation and its limits pointed out by Cockshott et al., in their book. Paraphrasing Turing (Turing was unimpressed by Wilkes’s EDSAC design, commenting that it was “much more in the American tradition of solving one’s difficulties by means of much equipment rather than by thought.”) a lot of appliances or code may not be often, a sustainable substitute for thoughtful architecture.

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