ISRA-Based Grouping: A Disk Reorganization Approach for Disk Energy Conservation and Disk Performance Enhancement

Liao, Xue-Liang; Bai, Shi; Wang, Yu-Ping; Hu, Shi-Min

doi:10.1109/tc.2010.153

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…Essary and Amer [43] proposed an on-line data block predict engine that implemented data block copy to reduce disk operations for energy savings by means of dynamic grouping. Liao et al [44] defined the concept of the Immediate Successor Relationship Amount (ISRA) to describe the successor relationships of data blocks. They proposed an ISRA-based grouping algorithm that reduced disk seek by grouping data blocks having frequent successive accesses and sorting these data blocks, with the main theory being to reduce disk seek and redistribute data from the angle of space.…”

Section: Related Workmentioning

confidence: 99%

Lazy scheduling based disk energy optimization method

Dong

Chen

Tang

et al. 2020

Tinshhua Sci. Technol.

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Reducing the energy consumption of the storage systems disk read/write requests plays an important role in improving the overall energy efficiency of high-performance computing systems. We propose a method to reduce disk energy consumption by delaying the dispatch of disk requests to the end of a time window, which we call time window-based lazy scheduling. We prove that sorting requests within a single time window can reduce the disk energy consumption, and we discuss the relationship between the size of the time window and the disk energy consumption, proving that the energy consumption is highly likely to decrease with increasing window size. To exploit this opportunity, we propose the Lazy Scheduling based Disk Energy Optimization (LSDEO) algorithm, which adopts a feedback method to periodically adjust the size of the time window, and minimizes the local disk energy consumption by sorting disk requests within each time window. We implement the LSDEO algorithm in an OS kernel and conduct both simulations and actual measurements on the algorithm, confirming that increasing the time window increases disk energy savings. When the average request arrival rate is 300 and the threshold of average request response time is 50 ms, LSDEO can yield disk energy savings of 21.5%.

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Section: Related Workmentioning

confidence: 99%

Lazy scheduling based disk energy optimization method

Dong

Chen

Tang

et al. 2020

Tinshhua Sci. Technol.

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show abstract

“…Finally, a group is sized such that it fits onto a disk track. Liao et al [2011] improve the data-grouping technique of Essary and Amer by putting the groups on the disk in the order that minimizes the seek time. Also, they eliminate data replication to avoid the complexity associated with maintaining data consistency.…”

Section: Popular Data Concentration Bymentioning

confidence: 99%

Power-reduction techniques for data-center storage systems

Bostoen¹,

Mullender²,

Berbers

2013

ACM Comput. Surv.

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As data-intensive, network-based applications proliferate, the power consumed by the data-center storage subsystem surges. This survey summarizes, organizes, and integrates a decade of research on power-aware enterprise storage systems. All of the existing power-reduction techniques are classified according to the disk-power factor and storage-stack layer addressed. A majority of power-reduction techniques is based on dynamic power management. We also consider alternative methods that reduce disk access time, conserve space, or exploit energy-efficient storage hardware. For every energy-conservation technique, the fundamental trade-offs between power, capacity, performance, and dependability are uncovered. With this survey, we intend to stimulate integration of different power-reduction techniques in new energy-efficient file and storage systems.

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“…These hard disks, which are not in use enables low-power mode and consumes no energy. Techniques for conserving storage energy have been comprehensively explored for more than ten years, and the research object mainly includes a single-disk-based mobile device (e.g., laptop) and a multiple-disks-based storage system in a data center [28].…”

Section: Memorymentioning

confidence: 99%

Increasing Cloud Usage: A Shift towards Green Clouds

Kaur¹,

Kinger²

2013

IJCA

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As Cloud computing is being adopted widely and usage of internet services is increasing day by day, which gives rise to built large scale data centers. In cloud computing, a lot of research has been done on energy saving programs for reducing the energy consumption of large scale data centers. As large scale data centers consumes large amount of electrical power which affects the operating cost, performance as well as environment. This paper surveys the different energy saving techniques in network, protocols, algorithms and hardware infrastructure for energy-efficient operations.

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ISRA-Based Grouping: A Disk Reorganization Approach for Disk Energy Conservation and Disk Performance Enhancement

Cited by 6 publications

References 25 publications

Lazy scheduling based disk energy optimization method

Lazy scheduling based disk energy optimization method

Power-reduction techniques for data-center storage systems

Increasing Cloud Usage: A Shift towards Green Clouds

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