Top Ten Questions. to Ask Your Primary Storage Provider About Their Data Efficiency. May Copyright 2014 Permabit Technology Corporation

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1 Top Ten Questions to Ask Your Primary Storage Provider About Their Data Efficiency May 2014 Copyright 2014 Permabit Technology Corporation

2 Introduction The value of data efficiency technologies, namely deduplication and compression, are well known in the data protection market where their disruptive impact launched an entire industry of disk-based backup appliances and software. Today s version of data efficiency has evolved beyond its backup predecessors and is now disrupting the primary storage market by driving down the effective cost and increasing the effective capacity of next generation, high performance enterprise storage, enabling primary storage systems to address rampant data growth. As data efficiency solutions are evaluated for adoption in primary storage systems, many vendors are realizing that they cannot simply port their backup deduplication technology to this use case. The applications are fundamentally different, and the solutions need to be architected specifically for primary storage implementations. This document outlines and compares various data efficiency methods, identifies implementation challenges, and arms buyers with questions that will help them evaluate data efficiency offerings. Data Efficiency Data efficiency has become a must-have capability for the latest generation of disk and flash storage across the data center and the cloud. In this section, we will focus on the most important areas of concern when evaluating this technology: performance, scalability and resource efficiency, reliability, maturity, and flexibility. Performance Unlike storage for backup, primary storage is characterized by high random access throughput and low latency. To be effective in primary storage, deduplication needs to scale with customers performance requirements and to run inline. For high-performance, flash-based storage devices, it must be able to operate at or above the 1M IOPS level when processing 4 KB blocks of data. Question)1:)At)what)granularity)(block)size))does)your)technology)recognize)duplicates?) Best)Answer:)4)KB)fixed)blocks.)) Studies have shown that deduplication rates increase dramatically with a smaller (preferably 4 KB) chunk size. Significant advances in the resource and performance optimization of today s high performance index engine make 4 KB chunk sizes the standard, and the majority of data efficiency solutions now operate at this level of granularity. Some vendors use larger chunks to make their development jobs easier, but larger chunks reduce overall dedupe effectiveness. Conversely, chunk sizes smaller than 4 KB can introduce performance issues if the indexing and data analytics are not optimized effectively. 4 KB chunk sizes deliver the best balance between data reduction and load on the system s performance. Single instancing (also referred to as file-level deduplication), which many vendors still use, was the initial approach used in the backup market. In single instancing, a hash is computed for each file. Changing even one byte triggers a new instance of the entire file, resulting in no opportunity for space savings between the many blocks in the file. Compared to 4 KB block level dedupe that optimizes multiple file systems or other raw storage, single instancing doesn t yield high levels of data efficiency. Top Ten Questions to Ask Your Primary Storage Provider About Their Data Efficiency 1

3 Variable block length deduplication is another method used by some vendors. This approach works well in a backup environment, but is poorly suited for primary storage because it sub-optimizes performance in exchange for a minimal efficiency gain when applied to heavy random IO loads often encountered in primary storage environments. Question)2:)Does)deduplication)occur)inline)or)postEprocess?) Best)Answer:)Inline.) With inline processing, deduplication decisions are made on the fly, as data arrives. Duplicate data is never written to media; this approach greatly benefits flash storage, which has a finite number of write cycles before it degrades. More importantly, inline processing enables immediate replication for data protection. Post-process algorithms write all data at least once to a staging storage cache and perform the deduplication analysis after the fact. This consumes valuable storage space and can more quickly exhaust the life expectancy of flash devices and substantially increase system resource requirements. Many OEMs offering post-processing deduplication recommend not using the feature when consistent and predictable performance is needed, because they are prone to experiencing unexpected performance fluctuations due to dedupe background processing. Be wary of vendors who recommend turning their dedupe off when predictable performance is required from their storage system, because their implementation is not using optimal performance techniques that are available in today s inline deduplication engines. Finally, post-process implementations are not able to meet the system requirements of rapidly changing data sets, such as when many hundreds of desktops are cloned in a VDI environment. With post-process dedupe, the system can run out of available cache storage space almost immediately and will be unable to catch up. Question)3:)What)algorithm)do)you)use)for)hashing)and)do)you)require)any)special)hardware?) Best)answer:)Support)is)provided)for)both)hardwareEassisted)and)softwareEonly)approaches.) Deduplication algorithms employ content hashing to recognize duplicate blocks. Hashing maps the content of a block to a concise value. SHA-256 is known as a cryptographic hash and will produce a match only if two blocks match exactly. Using SHA-256 is a great approach, but it suffers from being slow on commodity CPUs (typically 100 MB/s of 4 KB blocks can be processed per processor core) and being computationally intensive. It s unlikely that a SHA-256 software computation will meet primary storage expectations without specialized hardware. If a vendor decides to deploy SHA-256, they should also employ an ASIC or FPGA to accelerate hashing. In the absence of hardware acceleration, a fast (non-cryptographic) hash is the best solution. Fast hashes (e.g., MurmurHash3) can be computed 15-20X faster and performed completely in software. Using a single Xeon core, a typical software SHA-256 calculator might sustain data rates of 180 MB/s, whereas a MurmurHash3 calculator can sustain 3,000 MB/s. Since fast hashes are non-cryptographic, they do require a data read compare to verify matches. For deduplicated data blocks, you are essentially trading a write operation for a read (an operation that is generally faster, particularly on flash storage). Top Ten Questions to Ask Your Primary Storage Provider About Their Data Efficiency 2

4 Question)4:)Does)your)product)combine)deduplication)with)block)level)compression?) Best)Answer:)Yes.)Deduplication)and)compression)are)complementary)data)efficiency)technologies.) Many datasets see 2X data reduction from block-level compression techniques. However, when compression is combined with block-level deduplication, the benefits are magnified by as much as 17X. ESG Labs research shows that this is especially true in environments where large amounts of redundant data are stored (e.g., VM images) as well as in those with highly redundant workflows (e.g., database development and testing). The greatest benefit comes from solutions that combine the two data efficiency technologies. The key to saving the most when these two technologies are combined is to deduplicate first and then compress. Since compression works file by file and deduplication works across petabytes of data, running deduplication first removes all the duplicates that would have been compressed if compression were run first. This sequencing results in less computational load on storage systems and delivers greater compute savings as well as greater data reduction across broad data streams. Scalability and Resource Efficiency Today s primary storage devices support multiple PB of storage. Typically all this data is managed across multiple logical volumes. Capacity limitations on storage volumes typically range from 16 to 256 TB. It s highly unlikely any customer is going to accept reduced capacity limits in exchange for capacity optimization. It s more likely that customers will expect to support larger volumes with deduplication than without. Question)5:)How)far)back)can)your)product)recall)seeing)a)4)KB)data)block?) Best)answer:)256)TB)or)more.) All deduplication algorithms must keep a list of previously stored data blocks. As this list grows, many algorithms have trouble managing it efficiently. A good algorithm should be able to accommodate current and future data growth. A lower number implies that an appliance will miss dedupe opportunities because hash keys may have rolled off due to capacity limitations. In primary storage environments, data is frequently added, modified, and deleted. Providing deduplication in the midst of this high churn rate requires the ability to support a window size that takes this activity into account. ) Top Ten Questions to Ask Your Primary Storage Provider About Their Data Efficiency 3

5 Question)6:)How)much)DRAM)does)your)product)need)to)manage)a)256)TB)history?) Reliability Best)answer:)25)GB.) Why this matters: Use too much DRAM for deduplication and the product will not be able to scale and will be cost-prohibitive and inefficient. DRAM needs to be allocated for content fingerprints, reference maps, and reference counts because you want the fastest processing and I/O to manage these. Presuming a vendor says they can handle a 256 TB history, you ll want to understand how much DRAM they ll need to store it. Be wary of vendors that tell you they store the list on disk, as this will become a severe performance bottleneck. Many inefficient algorithms require so much DRAM that it s difficult or impossible to find servers with enough DRAM to accommodate them. Whether it s due to physical events or improper management practices, any hardware can shut down unexpectedly. As a result of an unplanned shutdown, a deduplication appliance will perform a series of actions before the volume can be brought back on line. If the unit is configured for high availability, failover must occur in less than 30 seconds to avoid timeouts. Question)7:)How)long)does)your)appliance)take)to)bring)a)volume)back)online)after)a)power)failure?) Best)Answer:)10)seconds)or)less.) Recovery time is often a function of volume size and the amount of generated metadata. Make certain that your vendor s answer includes both the recovery time and the volume size that you require for your operations. Question)8:)Is)metadata)kept)persistent)in)nonEvolatile)memory)or)on)disk?) Best)answer:)Metadata)is)kept)persistent)and)local)with)the)data)at)all)times.) Metadata is non-user data created during the deduplication process. Metadata is the Rosetta stone used to reconstruct original user data files. All metadata modifications must be kept persistent at the risk of catastrophic data loss. Systems that store metadata in DRAM should be avoided at all cost. If metadata is stored apart from primary storage, then this must be considered in failover planning. There are fewer issues if metadata is stored with user data. ) Top Ten Questions to Ask Your Primary Storage Provider About Their Data Efficiency 4

6 Question)9:)What)is)the)high)availability)(HA))strategy?) Best)Answer:)Fully)separate)appliances)deployed)on)separate)data)paths.) Why This Matters:) Data availability is a hallmark of enterprise storage. Users employ redundant drives, redundant paths, and clustered servers to ensure data is available 24/7. If the appliance lacks an HA configuration, (e.g., clusters of at least two appliances) data outages can last hours or days depending on the issue. Maturity and Flexibility Deploying any new feature in a system poses a risk that must be managed carefully. Implementing technology that is well tested, mature and in broad operational deployment serves to mitigate that risk because potential problems have already been identified and addressed. In addition, solutions that are designed to be implemented across a variety of technical environments and use cases will provide greater flexibility and can greatly reduce time to market. Question)10:)How)mature)is)the)code)or)product)offering)and)is)the)solution)available)in)a)format)that)integrates) easily)with)your)products?) Best)Answer:)Commercially)deployed)for)over)5E10)years)and)available)in)multiple)formats)with) varying)levels)of)integration)and)configurability.) Why this matters:) Protecting data integrity is a key requirement for primary storage. Deployment of technologies that have not been proven in production introduces a significant element of risk for OEMs. Mature offerings are anchored with multiple patents that provide a solid legal foundation for the innovations embedded. Having a range of integration options for a technology allows OEMs the opportunity to achieve goals associated with time to market, performance, and seamless integration across a broad portfolio of storage products. Having flexible deployment options also means the offering can be applied not only to the latest product line but also to legacy product lines, enabling providers to extend product lifecycles and reduce development overhead. Look for data efficiency solutions that offer flexible deployment structures (ranging from plug-and-play models to deep integration) that meet your resource, performance, and time to market requirements. In some cases, a C language library with ultra-high index performance may be needed to upgrade an existing thin provisioning solution; in other cases more turn-key offerings may be needed to reduce development times or to create an opportunity to insert data optimization in less advanced storage devices. Top Ten Questions to Ask Your Primary Storage Provider About Their Data Efficiency 5

7 Conclusion The rampant rate of data growth poses many challenges for primary storage users. This document has discussed the impact that data efficiency technologies can have upon primary storage offerings, by reducing the effective cost and increasing the effective capacity of next generation, high performance enterprise storage. While any amount of data reduction is a positive step, there are significant differences in data efficiency solutions currently on the market. Differences in the way data efficiency solutions are designed and implemented can make this technology a purely nominal addition to a primary storage solution or a complete game changer. Best-of-breed data efficiency solutions can maximize and enhance industry leading primary storage. Such solutions operate inline at high performance (IOPS), use minimal resources, and scale to multiple PB. Vendors who embrace today s best-of-breed requirements will clearly differentiate their product offerings, gain competitive advantage and sell more storage at higher margins. About Permabit Technology Corporation Permabit Technology Corporation pioneered the development of high-performance data efficiency software for primary storage. We invent, develop, and deliver disruptive software for high-impact OEM product differentiation. With 32 patents in data efficiency, Permabit leads the industry in performance, scale, and resource efficiency via innovation in data deduplication, compression, thin provisioning, and replication technologies. We offer the gold standard in data efficiency and are continuing to evolve our offerings to meet the demands of today s storage industry. To learn more about Permabit s family of data efficiency products visit Top Ten Questions to Ask Your Primary Storage Provider About Their Data Efficiency 6

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