TY - JOUR
T1 - On reducing energy management delays in disks
AU - Krish, K. R.
AU - Wang, Guanying
AU - Bhattacharjee, Puranjoy
AU - Butt, Ali R.
AU - Gniady, Chris
N1 - Funding Information:
Ali R. Butt is an Associate Professor of CS at Virginia Tech. His research interests are in experimental computer systems, especially in data-intensive high-performance computing (HPC) and the impact of technologies such as massive multi-cores, Cloud Computing, and asymmetric architectures on HPC. Ali is a recipient of the NSF CAREER Award (2008), an IBM Faculty Award (2008), an IBM Shared University Research Award (2009), a Virginia Tech College of Engineering “Outstanding New Assistant Professor” Award (2009), a best paper award (MASCOTS 2009), and a NetApp Faculty Fellowship (2011). Ali was an invited participant (2009, 2012) and an organizer (2010) for the NAE’s US Frontiers of Engineering Symposium. He is a member of USENIX and ASEE, and a Senior Member of ACM and IEEE.
Funding Information:
This material is based upon work supported by the NSF under Grant No: CNS-1016408 , CNS-1016793 , CCF-0746832 , and CNS-1016198 .
PY - 2013
Y1 - 2013
N2 - Enterprise computing systems consume a large amount of energy, the cost of which contributes significantly to the operating budget. Consequently, dynamic energy management techniques are prevalent. Unfortunately, dynamic energy management for disks impose delays associated with powering up the disks from a low-power state. Systems designers face a critical trade-off: saving energy reduces operating costs but may increase delays; conversely, reduced access latency makes the systems more responsive but may preclude energy management. In this paper, we propose a System-wide Alternative Retrieval of Data (SARD) scheme. SARD exploits the similarity in software deployment and configuration in enterprise computers to retrieve binaries transparently from other nodes, thus avoiding access delays when the local disk is in a low-power state. SARD uses a software-based approach to reduce spin-up delays while eliminating custom buffering, shared memory infrastructure, or the need for major changes in the operating system. SARD achieves over 71% reduction in delays on trace-driven simulations and in an actual implementation. This will encourage users to utilize energy management techniques more frequently. SARD also achieves an additional 5.1% average reduction in energy consumption for typical desktop applications compared to the widely-used timeout-based disk energy management.
AB - Enterprise computing systems consume a large amount of energy, the cost of which contributes significantly to the operating budget. Consequently, dynamic energy management techniques are prevalent. Unfortunately, dynamic energy management for disks impose delays associated with powering up the disks from a low-power state. Systems designers face a critical trade-off: saving energy reduces operating costs but may increase delays; conversely, reduced access latency makes the systems more responsive but may preclude energy management. In this paper, we propose a System-wide Alternative Retrieval of Data (SARD) scheme. SARD exploits the similarity in software deployment and configuration in enterprise computers to retrieve binaries transparently from other nodes, thus avoiding access delays when the local disk is in a low-power state. SARD uses a software-based approach to reduce spin-up delays while eliminating custom buffering, shared memory infrastructure, or the need for major changes in the operating system. SARD achieves over 71% reduction in delays on trace-driven simulations and in an actual implementation. This will encourage users to utilize energy management techniques more frequently. SARD also achieves an additional 5.1% average reduction in energy consumption for typical desktop applications compared to the widely-used timeout-based disk energy management.
KW - Spin-up delay reduction Disk energy management Peer memory sharing
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U2 - 10.1016/j.jpdc.2013.02.011
DO - 10.1016/j.jpdc.2013.02.011
M3 - Article
AN - SCOPUS:84875692399
SN - 0743-7315
VL - 73
SP - 823
EP - 835
JO - Journal of Parallel and Distributed Computing
JF - Journal of Parallel and Distributed Computing
IS - 6
ER -