Towards energy proportional cloud for data processing frameworks
SustainIT'10 Proceedings of the First USENIX conference on Sustainable information technology
Dynamic grid quorum: a reconfigurable grid quorum and its power optimization algorithm
Service Oriented Computing and Applications
Reactive power management for distributed systems
Proceedings of the 49th Annual Southeast Regional Conference
Enabling consolidation and scaling down to provide power management for cloud computing
HotCloud'11 Proceedings of the 3rd USENIX conference on Hot topics in cloud computing
Analysis of disk power management for data-center storage systems
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
Systematic approach of using power save mode for cloud data processing services
International Journal of Ad Hoc and Ubiquitous Computing
Decreasing power consumption with energy efficient data aware strategies
Future Generation Computer Systems
Power-reduction techniques for data-center storage systems
ACM Computing Surveys (CSUR)
A three-phase energy-saving strategy for cloud storage systems
Journal of Systems and Software
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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.