Primas, Bernhard and Garraghan, Peter and McKee, David and Summers, Jon and Xu, Jie (2017) A Framework and Task Allocation Analysis for Infrastructure Independent Energy-Efficient Scheduling in Cloud Data Centers. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) :. IEEE, pp. 178-185. ISBN 9781538606933
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Abstract
Cloud computing represents a paradigm shift in provisioning on-demand computational resources underpinned by data center infrastructure, which now constitutes 1.5% of worldwide energy consumption. Such consumption is not merely limited to operating IT devices, but encompasses cooling systems representing 40% total data center energy usage. Given the substantivecomplexityandheterogeneityofdatacenteroperation spanning both computing and cooling components, obtaining analytical models for optimizing data center energy-efficiency is an inherently difficult challenge. Specifically, difficulties arise pertaining to the non-intuitive relationship between computing and cooling energy in the data center, computationally complex energy modeling, as well as cooling models restricted to a specific class of data center facility geometry - all of which arise from the interdisciplinary nature of this research domain. In this paper we propose a framework for energy-efficient scheduling to alleviate these challenges. It is applicable to any type of data center infrastructure and does not require complex modeling of energy. Instead, the concept of a target workload distribution is proposed. If the workload is assigned to nodes according to the target workload distribution, then the energy consumption is minimized. The exact target workload distribution is unknown, but an approximated distribution is delivered by the framework. The scheduling objective is to assign workload to nodes such that the workload distribution becomes as similar as possible to the target distribution in order to reduce energy consumption. Several mathematically sound algorithms have been designed to address this novel type of scheduling problem. Simulation results demonstrate that our algorithms reduce the relative deviation by at least 16.9% and the relative variance by at least 22.67% in comparison to (asymmetric) load balancing algorithms.