This paper presents a measurement-based case study of high-throughput computing, and points out the obstacles encountered by data center operators in their efforts to minimize energy consumption and carbon emissions, and offers actionable recommendations for decreasing the energy usage of servers.
Abstract
The significant energy consumed by data centers has become a concern both for costs and associated carbon emissions. In particular, the energy efficiency of servers is a key consideration for data center operators, and understanding servers'power consumption under different operating conditions is an important aspect of it. In this paper, we present a measurement-based case study of high-throughput computing. We analyze power usage information of an operational data center, combined with focused measurements of power reduction techniques for a representative high-throughput workload. The study points out the obstacles encountered by data center operators in their efforts to minimize energy consumption and carbon emissions, and discusses the impact of server configuration adjustments on the energy consumption of processing jobs. We offer actionable recommendations for decreasing the energy usage of servers, while considering both performance and carbon emissions.
It is proved that models trained using the system-level full features have the highest accuracy comparing to only use part of features, and it is proved that a multiple-variable linear regression approach is more precise than a CPU-only linear approach.
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