DocumentCode
2487735
Title
Low power mode in cloud storage systems
Author
Harnik, Danny ; Naor, Dalit ; Segall, Itai
Author_Institution
IBM Haifa Res. Labs., Haifa, Israel
fYear
2009
fDate
23-29 May 2009
Firstpage
1
Lastpage
8
Abstract
We consider large scale, distributed storage systems with a redundancy mechanism; cloud storage being a prime example. We investigate how such systems can reduce their power consumption during low-utilization time intervals by operating in a low-power mode. In a low power mode, a subset of the disks or nodes are powered down, yet we ask that each data item remains accessible in the system; this is called full coverage. The objective is to incorporate this option into an existing system rather than redesign the system. When doing so, it is crucial that the low power option should not affect the performance or other important characteristics of the system during full-power (normal) operation. This work is a comprehensive study of what can or cannot be achieved with respect to full coverage low power modes. The paper addresses this question for generic distributed storage systems (where the key component under investigation is the placement function of the system) as well as for specific popular system designs in the realm of storing data in the cloud. Our observations and techniques are instrumental for a wide spectrum of systems, ranging from distributed storage systems for the enterprise to cloud data services. In the cloud environment where low cost is imperative, the effects of such savings are magnified by the large scale.
Keywords
Internet; digital storage; large-scale systems; redundancy; cloud data services; cloud storage systems; full-power operation; large scale distributed storage systems; low power mode; low-utilization time intervals; redundancy mechanism; Availability; Clouds; Costs; Energy consumption; Energy management; Energy storage; Instruments; Large-scale systems; Power system reliability; Redundancy;
fLanguage
English
Publisher
ieee
Conference_Titel
Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on
Conference_Location
Rome
ISSN
1530-2075
Print_ISBN
978-1-4244-3751-1
Electronic_ISBN
1530-2075
Type
conf
DOI
10.1109/IPDPS.2009.5161231
Filename
5161231
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