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IBM has lately announced that it has brought a collection of latest enhancements to its XIV Storage system Gen3, including guide for a brand unique solid condition power (SSD) caching choice, that is declared to boost device efficiency by as much as thrice.
based on IBM, going for the IBM XIV SSD Caching selection provides up to 6TB of fast-examine SSD storage to its techniques, which is positioned between the outfit recollection and tough drives.
here's then used as a speedy cache for the most often accessed records, as a result dramatically reducing latency times when in comparison to mechanical storage.
"These newest updates to XIV Gen3 are a pretty expedient illustration of IBM's persevered commitment to helping customers obtain traffic effects via smarter storage technologies," spoke of Doug Balog, vice chairman and company line executive for IBM Storage systems.
"It comes entire the passage down to saving consumers time and money with extremely efficient, with ease managed, virtualized, and high-performing storage programs dote XIV Gen3," concluded the enterprise’s rep.
in addition to IBM XIV SSD Caching, IBM announced the skill to reflect statistics between previous versions of XIV and XIV Gen3 methods, on the passage to ease statistics migration and assist give valued clientele flexibility to repurpose their XIV models as catastrophe healing backup techniques.
The XIV Storage device Gen3 was delivered by IBM in the seat of ultimate year as a unique era of storage options designed to lessen the total can imbue of possession, and help device efficiency by means of four instances over the conventional model.
With XIV Gen3, IBM additionally changed the system's Ethernet-based mostly backplane with a high-efficiency InfiniBand backplane, that accelerated the device's inner bandwidth between modules by passage of forty times.
The XIV Storage system Gen3, in conjunction with its SSD Caching alternative, is purchasable right now from IBM, with pricing being supplied upon request.
IBM is bettering its Scale Out community connected Storage (IBM SONAS) system first launched in February, including policy-pushed file tiering, bigger skill drives and a gateway that integrates the NAS with the IBM XIV screen storage device.
furthermore, IBM has moved to the midrange with its virtual tape library (VTL) facts deduplication platform, launching the outfit Storage TS7610 ProtecTier Deduplication appliance categorical.
IBM previewed the tiering capabilities when it first announced the SONAS system, which is in line with IBM's accustomed Parallel File outfit (GPFS). They let purchasers bound records across storage tiers on disk and tape promptly in response to guidelines. here's enabled by using the SONAS coverage engine that IBM claims can scan 10 million data per minute per node and straight away scan billions of files when the nodes are utilized in parallel.The XIV gateway lets SONAS purchasers exhaust XIV disk for returned-conclusion storage, bringing SONAS scanning and tiering capabilities to the XIV Storage equipment.
all through IBM's earnings name Monday nighttime, the supplier's executives spoke of the XIV Storage system delivered greater than one hundred thirty consumers ultimate quarter and revenue more than doubled from final 12 months. IBM storage advertising manager Brad Johns mentioned he anticipates purchasers the exhaust of IBM SONAS with XIV Storage device to deliver screen and file storage together. "we will give well-nigh unified storage based on XIV," he stated. "The equal XIV may well exist providing screen statistics to a collection of transactions and built-in to SONAS for file storage."
SONAS additionally now supports 600 GB SAS drives – up from 450 GB – and asynchronous replication between two methods at any distance for catastrophe restoration.
Mike Kahn, an analyst at the Clipper neighborhood Inc., observed he considers it a positive that IBM is integrating the methods rather than selling entire of its storage as isolated structures.
"IBM has a vast richness of items, however there is a criticism that they haven't been tying these products to each and every different," Kahn observed. "So here's a positive component as far as i'm involved. I believe it's more for XIV shoppers who're in location, no longer to sell XIV in the back of the SONAS."
SONAS offers IBM two NAS systems – it additionally sells NetApp's FAS storage programs beneath the IBM N collection company. IBM's Johns celebrated the N collection is the company's platform for mainstream NAS, while SONAS is aimed at consumers "dealing with very large portions of information. certainly, excessive-performance computing is one of those markets. They view this as complementing their N series. In environments where managing numerous N sequence programs is attending to exist burdensome, this is the site this would healthy."
The Clipper community's Kahn celebrated the line between both products remains clear. "SONAS is a huge-scale answer," he observed. "if you're looking at anything of a modest dimension, their version of the NetApp product line is acceptable."
ProtecTier scales down, takes on EMC data area
The TS7610 ProtecTier Deduplication appliance categorical product is the first midrange virtual tape library IBM has delivered due to the fact it entire started delivery the dedupe VTL platform in 2008 after acquiring Diligent technologies Inc.
IBM's Johns talked about IBM has offered 800 licenses for the TS7650 ProtecTier commercial enterprise platform. The TS7610 is smaller (as much as four.5 TB of usable skill in 3U) and less expensive (beginning at $50,000) than the TS7650 and springs packaged with servers, adapters and storage. The TS7610 makes exhaust of SAS disk, while the TS7650 clients Fibre Channel drives. each own virtual tape library interfaces, however Johns referred to IBM is in view that types with Ethernet connectivity.
IBM referred to the smaller programs own a sustained throughput of 80 MBps, and up to 12 TS7610s can replicate to a TS7650 in a vital location.
Johns talked about the leading competitors for the TS7610 is EMC Corp.'s statistics domain DD6000 platform.
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The power Edge R730 is a two-socket 2U rack server with impressive processor performance, a large recollection footprint, extensive I/O options.
The R730 supports up to two Intel Xeon E5–2600 v3 processors, up to 24 DIMMs, and 16 difficult drives or SSDs.
The R730 has the traditional rack-mount server design with a heavy, robust and extremely well engineered all-metal chassis design with a lift-off lid for ease to service.Dell Power Edge R 730 front panel
The R730 supports up to 16 x 2.5” or up to 8 x 3.5” front-accessible, hot-plug difficult drives that are secured by a removable front bezel.
Other front-panel features include an interactive LCD control panel, USB management port/iDRAC Direct, a video connector and a vFlash media card slot.
Power-on indicator, power button: The power-on indicator lights when the system power is on. The power button controls the power supply output to the system.
NMI button :Used to troubleshoot software and device driver errors when running inevitable operating systems. This button can exist pressed using the quit of a paper clip. exhaust this button only if directed to enact so by qualified back personnel or by the operating system documentation.
LCD menu buttons: Navigate the control panel LCD menu
System identification button: The identification buttons on the front and back panels can exist used to locate a particular system within a rack. When one of these buttons is pressed, the system status indicator on the back flashes until one of the buttons is pressed again. Press to toggle the system ID on and off.
Video connector :Connect a VGA array to the system.
Information tag: A slide-out label panel which allows to record system information such as Service Tag, NIC, MAC address .
USB management port/ iDRAC Direct:Connect USB devices to the system or provides access to the iDRAC Direct feature
Hard drives: Up to eight 3.5 inch hot-swappable drives
USB connector: Connect USB devices to the system. The ports are USB 2.0-compliant.
Quick Sync (optional) :Indicates a Quick Sync enabled system. The Quick Sync feature is optional and requires a Quick Sync bezel. This feature allows management of the system using mobile devices. This feature aggregates hardware/firmware inventory and various system flush diagnostic/error information that can exist used in troubleshooting the system
Optical drive (optional): One optional SATA DVD-ROM drive or DVD+/-RW drive
vFlash media card slot :To insert a vFlash media card.
LCD panel: Displays system ID, status information, and system error messages. The LCD lights blue during prevalent system operation. The LCD lights amber when the system needs attention, and the LCD panel displays an error code followed by descriptive text.
The dell power Edge R730 is a ideal server for data warehouses, e-commerce, virtual desktop infrastructure (VDI), databases and high-performance computing HPC).
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X-Engine is a unique generation storage engine developed by Alibaba Database Department and is the basis of the distributed database X-DB. To achieve 10 times the performance of MySQL and 1/10 the storage cost, X-DB combines software with hardware to develop full exhaust of the most cutting-edge technical advantages in both software and hardware fields.
FPGA acceleration is their first attempt in the custom computing field. At present, the FPGA-accelerated X-DB has been theme to small-scale online grayscale release. FPGA will assist X-DB in the 6.18 and Double 11 shopping carnivals this year and will meet Alibaba's traffic departments' elevated database performance requirements.Overview of Alibaba's X-Engine
Owning the world's largest online transaction website, Alibaba's OLTP (online transaction processing) database system needs to meet high-throughput service requirements. According to their statistics, several billion records find written into their OLTP database system on a daily basis. During the 2017 Double 11 (Singles' Day) shopping carnival, the system's peak throughput reached 10 million TPS (transactions per second). Alibaba's traffic database systems mainly own the following characteristics:
To meet Alibaba's stringent requirements on performance and cost, they own designed a unique storage engine; it is called X-Engine. They own used many cutting edge database technologies in X-Engine; these include highly-efficient recollection index structures, asynchronous write assembly-line processing mechanism, and optimistic concurrency control for in-memory databases.
To achieve the best write performance and facilitate the separation of collected and sweltering data for tiered storage, X-Engine has borrowed the design of LSM-Tree. X-Engine maintains multiple memtables in its memory. It appends entire newly written data to these memtables, rather than directly replacing existing records. As the data storage is relatively large, it is impossible to store entire data in memory.
When data in recollection reaches a specified volume, they flush it to the persistent storage to shape an SSTable. To reduce latency in read operations, X-Engine regularly schedules compaction tasks to compact SSTables in the persistent storage. X-Engine merges key-value pairs in multiple SSTables by keeping only the latest version of key-value pairs if multiple versions exist (all key-value pair versions currently referenced by transactions will moreover exist kept).
Based on the characteristics of data access, X-Engine applies tiered storage to persistent data, where they store active data in relatively elevated data layers, and merge less active data (seldom accessed) with base-layer data and store it in the base-layer. It compresses base-layer data at a elevated compression rate and migrates it to storage media featuring large capacity but the relatively low charge (such as SATA HDDs) to achieve the goal of storing a large quantity of data at a relatively low cost.
In this case, tiered storage creates a unique problem: the system must frequently compact data, and the larger number of data writes requires more frequent compaction processes. Compaction is a compare and merge process which requires elevated consumption of CPU and storage I/O. In high-throughput write cases, a large number of compaction operations will occupy a large number of system resources. This can surely cause the performance of the entire system to drop tremendously thus leading to a huge repercussion on the application system.
The completely unique X-Engine has extraordinary multi-core expansion capability to achieve very elevated performance. Its front-end transaction alone can almost completely consume entire CPU resources, and it has a much higher resource using efficiency than InnoDB. They own shown the comparison between the two in the following figure:
At such a performance level, the system does not own any other resources for compaction operations; otherwise, performance levels will drop.
Based on their testing results, in DbBench benchmark's write-only scenario, the system periodically suffers from performance jitter. When a compaction stint occurs, the system performance drops by more than 40%, and when the compaction stint ends, the system performance returns to normal. They own shown this deportment in the following figure:
However, if they enact not conduct compaction promptly, the accumulation of multi-version data can seriously strike the read operations.
To solve the performance jitter caused by compaction, academic experts own achieve forward many structures such as VT-tree, bLSM, PE, PCP, and dCompaction. Although these algorithms can optimize the compaction performance across multiple aspects, they cannot reduce consumption of CPU resources by compaction. Based on apropos research statistics, when using SSD storage devices, the computing operations of compaction in the system consumes approximately 60% of computing resources. Therefore, no matter what optimizations they implement for compaction in the software layer, for entire LSM tree-based storage engines, performance jitter caused by compaction is always an Achilles' heel.
Fortunately, special hardware opens a unique door for solving performance jitter caused by compaction. In fact, it has become a trend to exhaust special hardware in solving traditional databases' performance bottlenecks. They own already offloaded database operations such as Select and Where to FPGA, and more tangled operations such as Group By are under research. However, the current FPGA acceleration solutions own a yoke of drawbacks:
To ease the repercussion of compaction on X-Engine's system performance, they own used an asynchronous hardware device FPGA, rather than the CPU to complete the compaction operation. This approach is crucial for a storage engine that satisfies stringent service requirements by maintaining the overall system performance at a high-level and avoiding performance jitters. Here are the major design features:
X-Engine's storage structure contains one or multiple recollection buffer areas (memtable), and multilayer persistent storage L0, L1... Each layer contains multiple SSTables.
When memtable is full, it turns into an immutable memtable and then flushes to an SSTable to L0. Each SSTable contains multiple data blocks and one index screen to index the data block. When it reaches the maximum number of L0 files, it triggers the merge of SSTables that own the overlapped key ranges; this process is called compaction. Likewise, when they attain the maximum number of SSTables at a layer, it merges with lower layer data. In this way, collected data constantly flows downward while sweltering data remains at a relatively higher layer.
We can specify a scope of key-value pairs that merge during a compaction process and this scope may contain multiple data blocks. Generally, a compaction process involves merging data blocks between two adjacent layers. However, they exigency to pay special attention to compaction tasks between L0 and L1. This is because as SSTables in L0 directly flushes from the memory, keys of SSTables in this layer may find overlapped. Therefore, a compaction stint between L0 and L1 may involve merging multiple data blocks.
For read operations, X-Engine needs to search for the required data from entire memtables. If it fails to find the data in memtables, it searches in the persistence storage, from higher to lower layers. As a result, timely compaction operations not only truncate the read path but moreover redeem the storage space. However, this mode uses a lot of system computing resources and causes performance jitter. This is an exigent problem that X-Engine must solve.FPGA Accelerated Database
From the perspective of the existing FPGA accelerated databases' status quo, they can divide FPGA accelerated database architectures into two types; the bump-in-the-wire design and the hybrid design. In the early stage, because of the FPGA card's insufficient recollection resources, the former nature of architecture is relatively popular. In this architecture, they site FPGA on the storage data path and exhaust the host as a filter. The odds is that it requires zero data replication, while the drawback is that the acceleration operation must exist a Part of the streamlined process, therefore making it not springy enough in terms of the design method.
The latter architecture design uses FPGA as a coprocessor, where they own connected FPGA to host via PCIe and exhaust the DMA mode for data transmission. As long as the offloading computation is intensive enough, data transmission costs are acceptable. The hybrid architecture design allows more springy offloading methods. For tangled operations such as compaction, data transmission between FPGA and host is necessary. Therefore, they own used the hybrid architecture design for hardware acceleration in their X-Engine.
In traditional LSM-tree-based storage engines, CPU is liable for handling prevalent user requests, as well as the scheduling and execution of compaction tasks. In other words, CPU is both the producer and consumer of compaction tasks. However, in a CPU-FPGA hybrid storage engine, CPU is only liable for producing and scheduling compaction tasks. In this method, they exigency to offload the execution of compaction tasks to the special hardware (FPGA).
For X-Engine, handling of prevalent user requests is similar to that of LSM-tree-based storage engines:
When L0 reaches the maximum number of SSTables, compaction gets triggered. They can divide offloading of a compaction stint into the following steps:
Compaction Units (CU) are the basic unit for FPGA to execute compaction tasks. An FPGA card can site multiple CUs, and each CU is composed of the following modules:
1. Decoder: In X-Engine, they store a KV in the data screen after compression and encoding. The primary role of the Decoder module is to decode KV pairs. Each CU contains 4 Decoders, and a CU back a compression stint of a maximum of 4 KV pairs. They exigency to split the compression tasks that require compression of more than 4 KV by the CPU. Based on their assessment, most compression tasks involve less than 4 KV pairs. They own placed 4 Decoders based on their considerations of performance and hardware resources. Comparing the configuration with 2 Decoders, we've increased 100% hardware consumption but obtained 300% performance improvement.
A compaction process contains three key steps: decoding, merging, and encoding. The most significant challenge for designing a proper compaction pipeline is that the execution time for each step varies significantly. For example, because of parallel processing, the throughput of the decoder module is much higher than the encoder module. Therefore, they must suspend some speedy modules to wait for downstream modules silent in the pipeline. To match the throughput differences in each of the pipeline's modules, they own designed a Controller module to coordinate different steps in the pipeline. An additional benefit of this design is that it decouples each module in the pipeline and enables more springy evolution and maintenance during engineering implementation.
When integrating FPGA compaction into X-Engine they hope to own independent CU throughput performance; the baseline of the experiment is the CPU.
Single-core compaction thread (Intel(R) Xeon(R) E5-2682 v4 CPU with 2.5 GHz)
We can draw the following three conclusions from the experiment:
Because a link request in FPGA is completed in milliseconds, using the traditional synchronous scheduling mode will cause elevated thread switching costs. Based on FPGA's characteristics, they own redesigned an asynchronous scheduling compaction method, where:
Asynchronous scheduling significantly reduces the thread-switching cost of CPU.
Fault Tolerance Mechanism Design
For FPGA compaction, the following three reasons can lead to the failure of compaction task:
To ensure the data is correct, the CPU will conduct computation again on entire failed tasks. As they mentioned earlier in the foible tolerance mechanism, they own addressed a little Part of compaction tasks that exceed the limits and own avoided the risk of FPGA internal errors.
Experiment Results Lab environment
We compared the performance of two storage engines:
We own included testing for InnoDB in this experiment (buffer size = 80 GB)
In this article, the X-Engine storage engine accelerated by FPGA brings 50% performance improvement for the KV interface, and 40% performance improvement for the SQL interface. With the lessen in the read/write ratio, FPGA's acceleration sequel becomes more obvious, thus meaning that FPGA compaction acceleration is suitable for write-intensive workloads. This is consistent with the intent of the LSM-tree design. Also, they own avoided FPGA's internal defects by designing a foible tolerance mechanism, and we've finally created a high-availability CPU-FPGA hybrid storage engine that meets Alibaba's actual service requirements.
It is the first actual project that uses a heterogeneous computing device introduced by X-DB to accelerate core database functions. Based on their experiences, FPGA can completely meet the computing demands raised by X-Engine's compaction tasks. At the selfsame time, they own been researching to schedule more suitable computing tasks to FPGA for execution, such as compression, BloomFilter generation, and SQL join operators. At present, the R&D for the compression role is completed, and it will exist built into a set of IP together with Compaction to accomplish data compaction and compression operations simultaneously.
X-DB FPGA-Compaction hardware acceleration is an R&D project completed by three parties; these parties are respectively the Alibaba Database Department database kernel team, the Alibaba Server R&D Department custom computing team, and Zhejiang University. Xilinx's technical team has moreover made Great contributions to the success of this project. They hereby extend their gratitude to them. They will post X-DB online for public beta this year. You will then exist able to suffer the significant performance improvement with FPGA acceleration to X-DB.
Today Penguin Computing announced immediate availability of the Penguin Tundra cluster platform powered by the unique Intel Xeon processor E5-2600 v3 product family. First unveiled at SC13 and based on Open Compute Project rack flush infrastructure, Penguin Tundra delivers the highest density and lowest total cost of ownership for elevated Performance Technical Computing clusters.
As the first Penguin-original system design Penguin Tundra is an embodiment of Penguin’s system integration and data seat expertise,” said Tom Coull, CEO and President of Penguin Computing. “Tundra provides density and TCO advantages for current large-scale HPC deployments while designed to accommodate future exascale HPC components such as coprocessors and fabrics.”
Penguin Tundra delivers a very elevated number of dual socket (2P) Intel Xeon E5-2600 v3 compute nodes per rack by combining the Open Rack infrastructure defined by the Open Compute Project with a Penguin original compute node design. Open Rack design utilizes the available physical space more efficiently and features moreover a shared, rack level, redundant AC/DC power conversion and distribution.
We are excited to note Penguin Computing offering state-of-the-art HPC solutions powered by the unique Intel Xeon E5-2600 v3 processors,” said Eoin McConnell, Xeon E5 family product line director, Data seat Group. “The enhanced floating point compute performance provided by the unique processors combined with the physical density and efficiency of the Penguin Tundra platform provides a powerful solution for TOP500 supercomputers and commercial HPC clusters.”
In this video from SC13, Phil Pokorny from Penguin Computing describes the company’s latest open compting innovations for HPC.
Penguin Computing is focused on testing, integrating and supporting Linux-based compute, storage and networking in the HPC clusters and Internet datacenters. The Penguin Tundra cluster solutions featuring Intel Xeon E5-2600 v3 processors, Penguin IceBreaker storage and Penguin Arctica switches are available immediately worldwide.
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