Optimal Performance-Aware Cooling on Enterprise Servers

Abstract

Datacenters house massive databases and applications to provide business decision support and cloud services where commercial success is contingent on timely responses. The servers that these tasks run on dissipate a lot of power, requiring equally powerful cooling systems to maintain a safe and efficient temperature level. In a typical enterprise server, server chassis fans can generate vibrations that are powerful enough to degrade the performance of data-intensive workloads. Our methodology measures and reproduces real-life vibrations on a rack server to evaluate the performance of different hard disks. Effective hardware management relies on an accurate understanding of these devices and their interactions to mitigate any performance degradation and meet thermal constraints. While current strategies focus on managing processing resources, at the expense of more data-dependent workloads, this paper approaches server efficiency by targeting the cooling-performance relationship in conjunction with other dependencies between power, thermal, and cooling. We extract a model from common database benchmarks based on expected resource utilization and corresponding cooling needs, while considering these mechanical disturbances. Our proposed strategy uses convex optimization to maintain thermal constraints at all times, while reducing the energy consumption of a server by 65% compared to basic proportional-integral-derivative controllers, or by 19% in comparison to advanced hardware management techniques proposed in literature.