Intra-disk Parallelism: An Idea Whose Time Has Come

Sankar,; Gurumurthi,; Stan, Mircea R.

doi:10.1109/isca.2008.11

Cited by 20 publications

(22 citation statements)

References 28 publications

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“…Major components, such as CPU and disk, are now under high scrutiny for improving their energy characteristics under varying utilizations [5,10,36]. Software efforts such as the Tickless Kernel project, aim to change the way the OS kernels operate when the server is idle [37].…”

Section: Related Workmentioning

confidence: 99%

Energy management for MapReduce clusters

2010

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The area of cluster-level energy management has attracted significant research attention over the past few years. One class of techniques to reduce the energy consumption of clusters is to selectively power down nodes during periods of low utilization to increase energy efficiency. One can think of a number of ways of selectively powering down nodes, each with varying impact on the workload response time and overall energy consumption. Since the MapReduce framework is becoming "ubiquitous", the focus of this paper is on developing a framework for systematically considering various MapReduce node power down strategies, and their impact on the overall energy consumption and workload response time.We closely examine two extreme techniques that can be accommodated in this framework. The first is based on a recently proposed technique called "Covering Set" (CS) that keeps only a small fraction of the nodes powered up during periods of low utilization. At the other extreme is a technique that we propose in this paper, called the All-In Strategy (AIS). AIS uses all the nodes in the cluster to run a workload and then powers down the entire cluster. Using both actual evaluation and analytical modeling we bring out the differences between these two extreme techniques and show that AIS is often the right energy saving strategy.

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Section: Related Workmentioning

confidence: 99%

Energy management for MapReduce clusters

2010

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“…As a result, random access times have only improved slightly. Moreover, the sequential I/O rate continues to grow with the square root of disk capacity since it depends on the disk platter density [15].…”

Section: Introductionmentioning

confidence: 99%

Low-power amdahl-balanced blades for data intensive computing

Szalay

Bell

Huang

et al. 2010

SIGOPS Oper. Syst. Rev.

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Enterprise and scientific data sets double every year, forcing similar growths in storage size and power consumption. As a consequence, current system architectures used to build data warehouses are about to hit a power consumption wall. In this paper we propose a novel alternative architecture comprising large number of so-called Amdahl blades that combine energyefficient CPUs with solid state disks to increase sequential read I/O throughput by an order of magnitude while keeping power consumption constant. We also show that while keeping the total cost of ownership constant, Amdahl blades offer five times the throughput of a state-of-the-art computing cluster for data-intensive applications. Finally, using the scaling laws originally postulated by Amdahl, we show that systems for dataintensive computing must maintain a balance between low power consumption and per-server throughput to optimize performance per Watt.

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“…Data striping, like RAID 0, represents the basic idea of inter-disk parallelism. However, even for hard drives, intra-disk parallelism has just started to be considered recently [28]. Since there is no mechanical movements in SSDs, there is a strong likelihood to improve internal parallelism.…”

Section: Related Workmentioning

confidence: 99%

“…Such parallelism is storagemedia independent, meaning it is effective for hard drive disks, SSDs, and tapes. Unlike inter-disk parallelism, intradisk parallelism is closely related to storage media, therefore, intra-SSD parallelism needs to be studied despite the fact that intra-HDD parallelism has already been well explored [28]. Interestingly, because flash-based storage has a unique mechanism, parallelism can be applied in multiple levels, namely, package-level, die-level, and plane-level [10].…”

Section: Introductionmentioning

confidence: 99%

Improving write performance by enhancing internal parallelism of Solid State Drives

Ruan

Zong

Alghamdi

et al. 2012

2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC)

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Abstract-Most researches of Solid State Drives (SSDs) architectures rely on Flash Translation Layer (FTL) algorithms and wear-leveling; however, internal parallelism in Solid State Drives has not been well explored. In this research, we proposed a new strategy to improve SSD write performance by enhancing internal parallelism inside SSDs. A SDRAM buffer is added in the design for buffering and scheduling write requests. Because the same logical block numbers may be translated to different physical numbers at different times in FTL, the on-board SDRAM buffer is used to buffer requests at the lower level of FTL. When the buffer is full, same amount of data will be assigned to each storage package in SSDs to enhance internal parallelism. To accurately evaluate performance, we use both synthetic workloads and realworld applications in experiments. We compare the enhanced internal parallelism scheme with the traditional LRU strategy since it is unfair to compare an SSD having buffer with an SSD without a buffer. The simulation results demonstrate that the writing performance of our design is significantly improved compared with the LRU-cache strategy with the same amount of buffer sizes.

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Intra-disk Parallelism: An Idea Whose Time Has Come

Cited by 20 publications

References 28 publications

Energy management for MapReduce clusters

Energy management for MapReduce clusters

Low-power amdahl-balanced blades for data intensive computing

Improving write performance by enhancing internal parallelism of Solid State Drives

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