Understanding the ESVA Architecture

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1 Understanding the ESVA Architecture Overview Storage virtualization is the basis of the ESVA (Enterprise Scalable Virtualized Architecture). The virtualized storage powered by the architecture is highly scalable in both capacity and performance. The performance scalability is not achieved by the traditional way of scaling up, but by the advanced technology of scaling out. By adding new storage systems to the virtualized configuration, both capacity and performance can be improved in one shot. For optimized capacity utilization and linear performance scaling, thin provisioning and load balancing technologies are implemented on the virtualized foundation. In this document, we will illustrate the three building blocks of the ESVA architecture and what problems they address in the IT environment. Figure 1. ESVA Architecture Storage Virtualization Storage virtualization is a technology to aggregate resources on different physical storage systems into a single storage pool. 1 (See Figure 2) 1 For the three ways to implement storage virtualization, please refer to the last section, Ways to Implement Storage Virtualization.

2 Figure 2. Storage Virtualization The aggregated capacity is presented to applications in the form of virtual data volumes. When applications are using the capacity, they can disregard where the capacity physically resides. Problem Addressed I Dispersed Free Space Low storage utilization has always been a prominent problem in the IT environment. With direct-attached storage (DAS), the utilization is about 30%. Even if DAS is consolidated into a storage area network (SAN), the utilization is still often no more than 50%. One of the main reasons accounting for low utilization is that the free capacity dispersedly located on different physical systems can t be consolidated for uses. As shown in Figure 3, in SAN configuration, there is 5TB of free space left on the two storage systems. Figure 3. Dispersed Unused Space

3 However, if users want to introduce a new application requiring 4TB in, the physical boundary makes them unable to consolidate the dispersedly located capacities into a data volume for uses. Users will therefore need to add additional capacity for the new application, and this will reduce utilization. Storage virtualization technology eliminates the problem since the storage consolidation it enables can overcome physical boundaries. As shown in Figure 4, in the virtualized environment, the consolidated 5TB of free space can be easily leveraged by the newly-introduced application as a virtual data volume. Figure 4. Consolidated Unused Space Problem Addressed II Painful Capacity Scaling Whether through direct installation or expansion enclosure attachment, adding disk drives is always the first step when users need more storage capacity. However, every system has its own limitation in the number of drives it can carry. When the threshold is reached, the painful process of capacity scaling begins. First of all, users need to buy a new system able to house more drives. Their purchasing options are often limited. For example, if they now use a system able to accommodate 50 drives, the next-level upgrade option may be a system housing 100 drives. Even if the capacity is much more than what they need in the near future, they have no choice but to make the large upfront investment. Then, before replacing the old system with the new one, users have to schedule downtime to do data migration. According to researches, planning and staging data migration takes 2 to 4 weeks and moving 1TB of

4 data costs $5,000. When the migration is finally done and the service again online, it has taken much time and money. In a virtualized environment, capacity scaling is extremely simple. Instead of replacing old systems with new ones, the scaling is achieved by adding new systems to the current configuration. To expand the storage pool, users only need to connect a new system and add its capacity to the pool. The configuration process requires neither downtime nor manual data migration. All online operations can run normally. The ESVA architecture also allows the scaling to be flexibly done in an incremental, as-needed manner. Take the ESVA-F60 as example. A single ESVA-F60 system can house 16 drives. By attaching to the maximum of six expansion units, each housing 16 drives, it can be scaled to an installation housing 112 drives. When more capacity is needed, users can add another ESVA-F60 system to the pool for the additional space provided by 16 drives, and later keep on scaling on the16-drive basis for future needs. From the above, we have learned how storage virtualization consolidates space and simplifies capacity scaling. In the following, we will further illustrate how the thin provisioning technology optimizes utilization efficiency of the consolidated capacity. Thin provisioning Thin provisioning is a technology to allocate just-in-time capacity to applications when data are written. It largely improves utilization by eliminating allocated but unused space. In the figure below, we can see a storage pool of 10TB.

5 Figure 5. Thin Provisioning Thin provisioning allows users to bluff capacity to applications so they can prescribe 20TB of space to host 1 and 30TB to host 2. The prescription does not fix any amount of space for the applications. The pooled capacity is dynamically allocated when data writes occur. Seeing the total capacity is using up, users can expand the pool on-the-fly and the added capacity will be automatically leveraged by applications. The process is totally transparent to the online service. Problem Addressed Allocated but Unused Space In the traditional way of provisioning, for an application to leverage a certain amount of capacity, IT managers have to first create a data volume of the amount and then allocate it to the host. The act of allocation means privileging a certain host to use a certain amount of capacity. Since expanding data volumes causes downtime, IT managers tend to create data volumes of the amount much larger than immediately required to delay expansion hassles. As shown in Figure 6, host 1 currently has only 5TB of data but is assigned a data volume of 20TB. And host 2 currently has 3TB of data but is assigned a volume of 10TB.

6 Figure 6. Traditional Provisioning The 22TB of free space is wasted because the allocation prevents other applications from using it. These large and underutilized data volumes lead to the storage utilization as low as 27%. They also inflict much wasted investment in space, power and cooling expenses. With the technology of thin provisioning, since prescription does not mean fixing the use of physical capacity, storage utilization can be made as high as 80 to 90%.

7 Figure 7. High Utilization with Thin Provisioning In addition, the management efforts spent on forecasting, planning, pre-allocating and monitoring individual data volumes are no longer necessary. IT managers need only to keep a check on the utilization of the storage pool. Load balancing In a general sense, the idea of scale-out means to linearly increase performance by adding multiple computing nodes. Scale-out has been a popular architecture for servers for years but is relatively new for storage. To achieve scale-out with virtualized storage, the ESVA automatically balances workloads on the storage systems composing the storage pool by evenly distributing I/Os. As shown in Figure 8, when the host issues a write request composed of six data blocks to the storage pool, the blocks will be evenly distributed on the three member storage systems, with each member receiving two blocks.

8 Figure 8. Load Balancing The even way of distribution ensures applications to leverage all computing power in a virtualized configuration. When a member is added or removed, to maintain optimal performance, the existing data would be evenly migrated. In Figure 9, originally there are three data blocks on each of the two member systems. When a new member is introduced in, the data blocks would be migrated to make all members equally share the workloads. Figure 9. Balanced Data Migration Problem Addressed: Painful Performance Scaling The problems of performance scaling are similar with those of capacity scaling. When the performance limitation of a single storage system is reached, the traditional way to achieve higher performance is to replace the old system with a new one. Users scaling choices are limited by what vendors offer. For example, even if users think they currently need only two times better

9 performance, they may have to buy a new system delivering 5 to 6 times better performance because it is the only available next-level upgrade choice. What s worse, the new system they spend much upfront investment on often come with poorer price/performance than the old system. As illustrated in the part of Painful Capacity Scaling, the process of system replacing also involves data migration, and it takes much time and money. Moreover, after the migration is done, the old system will be cast away or used for secondary applications. It can t contribute to serving necessary performance for the primary application. Now with the ESVA, users can enjoy flexible scaling along with growing needs. As shown in the figure below, scaling for several times better performance is as simple as adding new storage systems into the storage pool. Figure 10. Linear Performance Scaling Adding one more system means two times better performance while adding two more means three times better. The scale-out architecture requires no manual data migration and maintains good price/performance. The ESVA enables users to achieve necessary performance in the most simple and cost-effective way. Ways to Implement Storage Virtualization On the market, there are three basic approaches to implement storage virtualization: server-based, appliance-based and storage-based. Server-based storage virtualization is realized by software running on a server. The virtualization scope includes any storage visible to the server, whether it is directly attached or network attached.

10 Appliance-based virtualization is realized by either intelligent switch devices or dedicated hardware appliances. The former is called in-band solution, and the latter out-of-band. The two solutions both pool storage resources and present them to hosts, but in-band solution is located in the data path between hosts and storage, while out-of-band solution is located out of. The last way to realize storage virtualization is to embed the technology in the storage system itself, and that s also the way the ESVA adopts. Virtualized storage resources are available to any host that can access the system. Comparison of the three implementations is as the following: Way of Implementation Benefits Defects Server-based Supports multivendor storage; may leverage familiar server management tools Server overhead; software installation on each server; multiple points of management; dependent on server operating systems Appliance-based Supports multivendor storage; optimizes Complicated deployment; additional hardware investment; performance Storage-based Easy deployment; simplifies management and data protection; no extra hardware or software Single vendor storage According to the comparison above, we can see that storage-based virtualization is short in its heterogeneous capability. However, an online survey done by Forrester suggests that more than 90% of users adopt storage from no more than two vendors. (See Figure 11)

11 Figure 11. Number of Vendors Supplying Storage Used in Virtual Server Environment In the simple storage environment mostly leveraging single-vendor products, storage-based virtualization is the best way to help users enjoy storage virtualization benefits with minimized extra costs, deployment hassles and management efforts. Server- or appliance-based virtualization are not suitable solutions because users can t benefit much from the multivendor storage support they offer but have to suffer much from the additional expenses and deployment and management hassles they require. Featuring storage-based storage virtualization, the ESVA is an ideal choice for mid-range enterprises. At affordable costs, it can help rising mid-range enterprises to make the most of their storage investment to enhance business competitiveness.

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