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Solid State Drive Applications in Storage and Embedded Systems

SSD in Embedded Storage Applications

Emerging markets in Internet Protocol Television (IPTV), Video on Demand (VoD), the Digital Cinema Initiative (DCI), and Web 2.0 are bringing more on-demand high definition content to a broader base of users. This means that increased capacity and performance density is required from embedded devices as well as head-end, content distribution, and edge service systems. Storage in embedded mobile devices such as High Definition (HD) digital cameras (1920 x 1080, 2048 x 1080, Digital Cinema 4096 x 2160, Red Scarlet 6000 x 4000 high-resolution frame formats) and consumer devices are pushing embedded storage requirements to terabyte levels. Likewise, capacity requirements for head-end digital media services are reaching petabyte levels. For example, one hour of HD content un-encoded raw format for 1080p at 24 fps would require 489 gigabytes. A library of that content with 10,000 hours would require around 5 petabytes of formatted capacity. Most often video is encoded for delivery to consumer devices, with compression ratios that are 30 to 1 or more. Even with encoding, a library of 100,000 hours (similar to total content at Netflix) encoded in typical high definition distribution/transport format requires 2 to 4 gigabytes per encoded hour on average, so at least 200,000 gigabytes or 200 terabytes total storage. Because of the multiplicity of transport encodings, content is stored in many formats, so capacity requirements are increased again. In this next section, we'll analyze how combinations of SSD and high capacity and performance density hard disk drives (HDDs) in tiered storage can help eliminate storage and I/O bottlenecks from both embedded and server systems and make the all-digital-content revolution a reality.

The increased capability of embedded storage and transport I/O in consumer devices has enabled the consumption of content at much higher bit rates. Progress in this embedded system segment has created demand for more high definition content from deeper content libraries. The emergence of affordable SSD for laptops over the next few years will help accelerate the demand for more on-demand high definition content. This means that the sources of content, starting with cameras, post-production, distribution, and finally delivery to consumers must all likewise upgrade to keep up.

Today, most storage applications utilize HDDs, sometimes with redundant arrays of inexpensive disks (RAIDs) to scale capacity and performance. Embedded storage applications often make use of flash devices to store digital media, and small form factor HDDs to store larger content libraries. Content is often distributed on IEEE 1394 (such as FireWire*), USB 2.0 (Universal Serial Bus), or eSATA (external Serial Advanced Technology Attachment) external HDD when capacity is an issue, but this is less portable and often a significant I/O bottleneck. Media flash devices provide great I/O performance, but with very limited capacity (64 gigabytes is a typical high end device). For portable or semi-portable capacity and performance density, SSDs and SSD arrays will help change the landscape for portable storage architectures scaling to terabytes of capacity. As SSD cost continues down, the convenience, performance density, power, and durability of SSDs will likely drive mobile content storage completely to SSD. For system level content management with petabyte scale requirements, it is unlikely that SSD will replace HDDs for a very long time.

Today, most tiered storage moves content between flash media or SSD tiers and HDD tiers at a file level, with users actively managing how content is allocated between HDD and SSD tiers.

If we look at a 2K/4K format digital video camera typically used in cinema today, these cameras can produce 250 Megabits per second (Mb/sec) in JPEG 2000 (Joint Photographic Expert Group) format, which is about 25 MB/sec or 90 GB/hour. Today's 2.5" SFF mobile class HDDs can keep up with this data rate and have capacities up to 500 gigabytes, which provides reasonable capture support for a single camera. The drawbacks though are that one HDD cannot support multiple cameras, they have lower MTBF (Mean Time Between Failure) when used in harsh environments (often the case in filming), and they are slower to download from the HDD to a backup RAID for post production. Some cameras support raw 2K/4K video capture, which is 53-MB per frame and at 30 frames/sec, 1.5-GB/sec data capture per stream. These types of emergent capture rates will require solid-state storage solutions.

SSDs offer high-end digital 2K/4K/6K cameras the same advantages that smaller flash media provide consumers, but at capacities (160GB for Intel X25-M SATA Solid-State Drive) that now make this a competitive option to HDD capture. This capacity offers approximately 2 hours of filming time and a capacity density that is competitive with SFF HDDs. The SSDs in this case would replace camera HDDs and offer lower power operation, equating to longer battery life, durability for filming in harsh environments, and high speed downloads to post-production RAID systems. The read rate of an Intel X25-E or X25-M SATA Solid-State Drive in sequential mode is at least four times that of typical SFF HDDs, so the download time will be far less. Even at raw 2K/4K rates of 1.5-GB/sec for uncompressed video ingest, it only requires 8 X25 SSDs to achieve full performance, however, at today's capacities (160 GB/SSD), the duration of ingest would only be 14 minutes (1.28 terabytes total SSD capacity for RAID0 mapping). One hundred percent ingest, rather than more typical 50 percent/50 percent write/read workloads is also a challenge for today's SSDs. Hybrid solutions with HDD backing SSD where SLC SSD is used as an ingest FIFO are perhaps a better approach and discussed in more detail in upcoming sections of this article.

The packaging of flash media into 2.5" and 1.8" SFF SAS/SATA (Serial Attached SCSI/Serial Advanced Technology Attachment) drives that are interchangeable with current SFF HDDs will help SSD adoption in the embedded segment of the digital media ecosystem. The SCSI (Small Computer System Interface) command set or ATA (Advanced Technology Attachment) command sets can both be transported to HDDs or SSDs over SAS with SATA tunneling protocols. This provides a high degree of interoperability with both embedded applications and larger scale RAID storage systems. As SSD cost per gigabyte is driven down and durability and maximum capacity per drive driven up by adoption of SSDs on the consumer side, the attractiveness of SSD replacement of HDDs for cameras will increase. Building hybrid arrays of SSD and HDD even for mobile field arrays provides a much better adoption path where cost/benefit tradeoffs can be made and systems right-sized. A key factor to success however is the development of software that can manage tiered SSD/HDD storage arrays for smaller mobile systems. This is even more important for post production, content delivery services, and the head-end side of the digital media ecosystem and will be covered in more detail in the following sections of this article. .

Since magnetic media storage density has kept pace with Moore's Law, both storage consumers and the storage industry have focused on cost per gigabyte and capacity density as the key metric. However, access to that stored data in general has not kept pace. Most often access performance is scaled through RAID systems that stripe data and protect it with mirroring or parity so that more HDD actuators can be used in parallel to speed up access. The upper bound for HDD random data access is in milliseconds, which has meant that the only way to scale access to storage is to scale the number of spindles data is striped over and to pack more spindles into less physical space. RAID storage system developers like Atrato Inc. have adopted SFF HDDs to increase performance density of HDD arrays. The Atrato V1000 SAID (Self-Maintaining Array of Identical Disks) has 160 SFF HDDs (spindles) packed into a 3RU (rack unit) array. This is presently the highest performance density of any HDD RAID solution available. At the same time, the emergence of SSDs in capacities that approach HDD (today on can get a 160-GB Intel X25-M SATA Solid-State Drive compared to 500-GB 2.5" SATA HDD) and cost per gigabyte that is only ten times that of HDD, has made tiered hybrid storage solutions for terabyte and petabyte scale storage very attractive. Rather than direct HDD replacement, tiered storage solutions add SSDs to enhance HDD access performance. The key is a hybrid design with RAID storage that is well matched to SSD tier-0 storage used to accelerate data access to larger HDD-backed multi-terabyte or petabyte stores. The fully virtualized RAID10 random access, no cache performance of the Atrato V1000 array is up to 2-GB/sec at large block sizes with IOPs up to 17K at small block size (measured with an HP DL580 G5 controller, where the rate limiting factor is the PCI Express generation 1 and memory controller).

Today most storage includes RAM-based I/O cache to accelerate writes on data ingest and to provide egress acceleration of reads through I/O cache read-ahead and hits to frequently accessed data. However, read cache often does little good for workloads that are more random and because the RAM cache sizes (even at 256 to 512 GB) are a very small fraction of capacity compared to petabyte back-end RAID storage (far less than one percent). Likewise, the cache miss penalty for missing RAM and going to an HDD backend is on the order of a 1000 to 1 or more (microsecond RAM cache access compared to millisecond HDD access). So, misses in RAM cache are likely and the penalty is huge, making RAM cache a wasted expenditure.

Figure 5 shows access patterns to storage that range from fully predictable/sequential to full random unpredictable access. Both SSDs and the high spindle density solutions perform well for random access. The SSDs provide this with the best overall performance and capacity density compared even to the high density HDD arrays like the SAID if cost per gigabyte is not an issue. The most interesting aspect of both of these emergent storage technologies is that they provide a performance matched tier-0 and tier-1 for highly scalable storage. In summary, the SSDs are about ten times the cost per gigabyte, but ten times the capacity/performance density of the SAID and the SAID is ten times the capacity/performance density of traditional enterprise storage. This can further be combined with a 3.5" SATA lowest cost per gigabyte capacity tier-2 (archive) when very low cost infrequently accessed storage is needed.

In the following sections, we'll examine how to tier arrays with an SSD tier-0.

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Figure 5: Performance range of access patterns observed by ApplicationSmart Profiler (Source: Atrato, Inc., 2009)

In Figure 5, totally random workloads are best served by storage devices with high degrees of concurrent access, which includes both SSD flash and devices like the Atrato SAID with a large number of concurrent HDD actuators. The biggest challenge arises for workloads that are totally random and access hundreds of terabytes to petabytes of storage. For this case, the SAID is the most cost-effective solution. For much smaller stores with totally random access (such as hundreds of gigabytes to terabytes), SSD provides the best solution. It is not possible to effectively cache data in a tier-0 for totally random workloads, so workloads like this simply require mapping data to an appropriate all SSD or highly concurrent HDD array like the SAID based on capacity needed. The most common case however is in the middle, where data access is semi-predictable, and where SSD and HDD arrays like the SAID can be coordinated with intelligent block management so that access hot spots (LBA storage regions much more frequently accessed compared to others) can be migrated from the HDD tier-1 up to the SSD tier-0. Finally, for totally predictable sequential workloads, FIFOs (First-In-First-Out queues) can be employed, with SLC SSDs used for an ingest FIFO and a RAM FIFOs used for block read-ahead. The ingest FIFO allows applications to complete a single I/O in microseconds and RAID virtualization software is used to reform and complete I/O to an HDD tier-1 with threaded asynchronous I/O, keeping up with the low latency of SSD by employing parallel access to a large number of HDDs. The exact mechanisms Atrato has designed to provide optimal handling of this range of potential workloads is provided in more detail in upcoming sections after a quick review of how RAID partially addresses the HDD I/O bottleneck, so we can later examine how to combine SSDs with HDD RAID for an optimal hybrid solution.

The most significant performance bottleneck in today's storage is the HDD itself, limited by seek actuation and rotational latency for any given access, which is worst case when accesses are random distributed small I/Os. Most disk drives can only deliver a few hundred random IOPs and at most around 100 MB/sec for sequential large block access. Aggregating a larger number of drives into a RAID helps so that all actuators can be concurrently delivering I/O or portions of larger block I/O. In general an HDD has a mean time between failure (MTBF) somewhere between 500,000 and 1 million hours, so in large populations (hundreds to thousands of drives) failures will occur on a monthly basis (two or more drives per hundred annually). Furthermore, environmental effects like overheating can accelerate failure rates and failure distributions are not uniform. So, RAID-0 has been enhanced to either stripe and mirror (RAID-10), mirror stripes (RAID-0+1), or add parity blocks every nth drive so data striped on one drive can be recovered from remaining data and parity blocks (RAID-50). Advanced double fault protection error correction code (ECC) schemes like RAID-6 can likewise be striped (RAID-60). So RAID provides some scaling and removes some of the single direct-attached drive bottleneck, but often requires users to buy more capacity than they need just to get better access performance, data loss protection, and reliability. For example, one may have 10 terabytes of data and need gigabyte bandwidth from it with small request sizes (32 K), which requires 32,768 IOPs to achieve 1 GB/sec. If each of the drives in the RAID array can deliver 100 IOPs, I need at least 320 drives! At 500 GB of capacity per drive that is 160 terabytes and I only need 10 terabytes. One common trick to help when more performance is needed from the same capacity is to "short-stroke" drives whereby only the outer diameter of each drive is used which often provides a 25-percent acceleration based on the areal density of the media.

Virtualization of a collection of drives also requires RAID mapping and presentation of a virtual logical unit number (LUN) or logical disk to an operating system. This means that all I/O requested from the RAID controller must be re-formed in a RAM buffer and re-initiated to the disk array for the original request. The virtualization makes RAID simple to use and also can handle much of the error recovery protocol (ERP) required for reliable/resilient RAID, but comes at the cost of additional processing, store-and-forward buffering, and I/O channels between the RAID controller, the ultimate user of the RAID system (initiator), and the back-end array of drives. Applications not written with RAID in mind that either do not or cannot initiate multiple asynchronous I/Os often will not get full advantage of the concurrent disk operation offered by large scale RAID. Even with striping, if an application issues one I/O and awaits completion response before issuing the next, full RAID performance will not be realized. As shown in Figure 6, even if each I/O is large enough to stripe all the drives in a RAID set (unlikely for hundreds of drives in large scale RAID), the latency between I/O requests and lack of a queue (backlog) of multiple requests outstanding on the RAID controller will reduce performance.

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Figure 6: RAID set striping and striding example (Source: Atrato, Inc., 2009)

A much more ideal system would combine the capacity and performance scaling of RAID along with the performance density scaling of SSD in a hybrid array so that users could configure a mixture of HDDs and SSDs in one virtualized storage pool. In order to speed up access with 10 terabytes of SSDs, one would have to combine 64 SSD drives into a virtualized array and stripe them with RAID-0. If they wanted data protection with RAID-10 it would increase the number of SSDs to 128. Even with lowering costs, this would be an expensive system compared to an HDD array or hybrid HDD+SSD array.

The bottleneck in embedded systems can be avoided by simply replacing today's HDDs with SSDs. The superior random read (and to a less extent write) provides a tenfold performance increase in general, albeit at ten times the cost per gigabyte. For small scale storage (gigabytes up to a few terabytes) this makes sense since one only pays for the performance increase needed and with no excess capacity. So, for embedded systems, the solution is simple drive replacement, but for larger capacity systems this does not make economic sense. What SSDs bring to larger scale systems is a tier that can be scaled to terabytes so that it can provide a 1-percent to 10-percent cache for 10 to 100 terabytes per RAID expansion unit (or SAID in the case of the Atrato Inc. system). Furthermore, the Intel X25-E and X25-M SATA Solid-State Drive SFF design allows them to be scaled along with the HDD arrays using common SFF drives and protocols. An intelligent block-level managed solid-state tier-0 with HDD tier-1 can then accelerate ingest of data to a RAID back-end store, sequential read-out of data from the back-end store, and can serve as a viable cache for the back-end HDD store that is much lower cost than RAM cache. In the following sections we will look at how SSDs are uniquely positioned to speed up HDD back-end stores geometrically with the addition of intelligent block management and an SSD tier-0.

The tiered approach described in the previous section can be managed at a file level or a block level. At the file level, intelligent users must partition databases and file systems and move data at the file container level based on access patterns for files to realize the speed-up made possible by tiers. Automated block level management using intelligent access pattern analysis software provides an increased level of precision in managing the allocation of data to the SSD tier0 and allows for SSD to be used as an access accelerator rather than a primary store. This overcomes the downside of the cost per gigabyte of SSDs for primary storage and makes optimal use of the performance density and low latency that SSDs have to offer.

Figures 7 through 9 show the potential for a coordinated SSD tier-0 with HDD tier-1 that is managed and virtualized by the Atrato Inc. virtualization engine. Figure 7 shows ingest acceleration through an SLC FIFO. Figure 8 shows sequential read-ahead acceleration through a RAM FIFO that can be combined with an MLC SSD semi-random read cache. The semi-random access SSD read cache has read hit/miss, write-through, and write-back-to-SSD operations. It can also be pre-charged with known high access content during content ingest. Any high access content not pre-charged will be loaded into SSD as this is determined by a TAM (Tier Access Monitor) composed of a Tier block manager and tier-0 and tier-1 access profile analyzers.

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Figure 7: Ingest I/O reforming using SLC SSD and Egress read-ahead RAM cache

Ingest I/O acceleration provides a synergistic use of SSD performance density and low latency so that odd size single I/Os as shown in Figure 8 can be ingested quickly and then more optimally reformed into multiple I/Os for a RAID back-end HDD storage array.

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Figure 8: Ingest I/O reforming using SLC SSD and Egress read-ahead RAM cache (Source: Atrato, Inc., 2009)

Likewise, for semi-random access to large data stores, SSD provides a tier-0 block cache that is managed by the TAM profile analyzer and intelligent block manager so that the most frequently accessed LBA ranges (hot spots) are always replicated in the SSD tier-0. Figure 9 shows one of the many modes of the intelligent block manager where it replicates a frequently accessed block to the SSD tier-0 on a read I/O—the profile analyzer runs in the I/O path and constantly tracks the most often accessed blocks up to a scale that matches the size of the tier-0.

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Figure 9: MLC SSD Tier-0 read cache opportunistic load of high access blocks on a read request

Overall, Figure 9 shows one mode of the intelligent block manager for write-back-to-SSD on a cache miss and HDD back-end read. The intelligent block manager also includes modes for write-through (during content ingest), read hits, and read misses. These tiered-storage and cache features along with access profiling have been combined into a software package by Atrato called ApplicationSmart and overall forms a hybrid HDD and SSD storage operating environment.

This design for hybrid tiered storage with automatic block-level management of the SSD tier-0 ensures that users get maximum value out of the very high performance density SSDs and maximum application acceleration while at the same time being able to scale up to many petabytes of total content. Compared to file-level tiered storage with an SSD tier-0, the block-level tier management is a more optimal and precise use of the higher cost, but higher performance density SSDs.

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