Conference Paper

HEaRS: A Hierarchical Energy-Aware Resource Scheduler for Virtualized Data Centers.

DOI: 10.1109/CLUSTER.2011.60 Conference: 2011 IEEE International Conference on Cluster Computing (CLUSTER), Austin, TX, USA, September 26-30, 2011
Source: DBLP

ABSTRACT With the increasing popularity of Internet-based cloud services, energy efficiency in large-scale Internet data centers has become important not only to curtail energy costs and alleviate environmental concern, but also because such systems can quickly reach the limits of power available to them. This paper investigates to what extent and how energy usage improvements through consolidation can benefit from taking into account the environmental influences and effects seen in data center systems. Toward that end, we present experimental results obtained in a fully instrumented, small scale data center and then use these results to propose a hierarchical energy-aware resource scheduler (HEaRS) for cluster workload placement and server provisioning, also considers the physical environment in which data center systems operate. Specifically, at the rack level, HEaRS tries to maintain a 'thermal balance' across the rack to avoid hot spots and reduce cooling costs. At the chassis level, HEaRS utilizes the proportional plus integral controller to achieve a balance in the levels of usage of electrical current between the two power domains in the chassis, which helps the chassis reach its most energy efficient state. Finally, at server level, HEaRS can employ known methods like dynamic voltage and frequency scaling or core idling to reduce power consumption. This results in a hierarchical set of controllers that jointly, implement holistic solutions to energy-aware resource scheduling for an entire rack, and this hierarchical solution can then be further extended to entire data centers. Our initial experiment result show opportunities for gains, with up to 16% in energy usage compared to methods that are not aware of the physical environment and up to 15% improvements in application performance.

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