Consider Element-based Storage to Support Application-centric Strategies

What is Element-based storage?

Element-based storage is a new concept in data storage that packages caching controllers, self-healing packs of disk drives, intelligent power/cooling, and non-volatile protection into a single unit to create a building-block foundation for scaling storage capacity and performance. By encapsulating key technology elements into a functional ‘storage blade’, storage capability – both performance and capacity – can scale linearly with application needs. This building-block approach removes the complexity of frame-based SAN management and works in concert with application-specific function that resides in host servers (OSes, hypervisors and applications themselves).

How are Storage Elements Managed?

Storage elements are managed by interfacing with applications running on host servers (on top of either OSes or hypervisors) and working in conjunction with application function, via either direct application control or Web Services/REST communication. For example, running a virtual desktop environment with VMware or Citrix, or a highly-available database environment with Oracle’s ASM or performing database-level replication and recovery with Microsoft SQL Server 2008 – the hosts OSes, hypervisors, and applications control their own storage through embedded volume management and data movement. The application can directly communicate with the storage element via REST, which is the open standard technique called out in the SNIA Cloud Data Management Interface (CDMI) specification. CDMI forms the basis for cloud storage provisioning and cloud data movement/access going forward.

The main benefits of the element-based approach are:

  • Significantly better performance – more transactions per unit time, faster database updates, more simultaneous virtual servers or desktops per physical server.
  • Significantly improved reliability – self-healing, intelligent elements.
  • Simplified infrastructure – use storage blades like DAS.
  • Lower costs – significantly reduced opex, especially maintenance and service.
  • Reduced business risk – avoiding storage vendor lock-in by using heterogeneous application/hypervisor/OS functions instead of array-specific functions.

Action Item: Organizations are looking to simplify infrastructure, and an application-centric strategy is one approach that has merit. Practitioners should consider introducing storage elements as a means to support application-oriented storage strategies and re-architecting infrastructure for the next decade.

Rob Peglar is VP of Technology at Xiotech and a Xiotech Senior Fellow.  A 32-year industry veteran and published author, he leads the shaping of strategic vision, emerging technologies, defining future offering portfolios including business and technology requirements, product planning and industry/customer liaison. He is the Treasurer of the SNIA, serves as Chair of the SNIA Tutorials, as a Board member of the Green Storage Initiative and the Solid State Storage Initiative, and as Secretary/Treasurer of the Blade Systems Alliance.  He has extensive experience in storage virtualization, the architecture of large heterogeneous SANs, replication and archiving strategy, disaster avoidance and compliance, information risk management, distributed cluster storage architectures and is a sought-after speaker and panelist at leading storage and networking-related seminars and conferences worldwide.  He was one of 30 senior executives worldwide selected for the Network Products 2008 MVP Award.    Prior to joining Xiotech in August 2000, Mr. Peglar held key technology specialist and engineering management positions over a ten-year period at StorageTek and at their networking subsidiary, Network Systems Corporation. Prior to StorageTek, he held engineering development and product management positions at Control Data Corporation and its supercomputer division, ETA Systems.     Mr. Peglar holds the B.S. degree in Computer Science from Washington University, St. Louis Missouri, and performed graduate work at Washington University’s Sever Institute of Engineering.  His research background includes I/O performance analysis, queuing theory, parallel systems architecture and OS design, storage networking protocols, clustering algorithms and virtual systems optimization.

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