Taming and Controlling Performance and Energy Trade-Offs Automatically in Network Applications

Abstract

In this paper, we demonstrate that a server running a single latency-sensitive application can be treated as a black box to reduce energy consumption while meeting a Service-Level Agreement (SLA) target. We find that it is possible to identify “sweet spot” settings for packet batching and processing rate control. These settings represent optimal trade-offs between the software stack and hardware. Specifically, they account for both the arrival rate and the composition of requests being served. By testing a few combinations of these settings on the live system, a proof-of concept controller can dynamically find settings that reduce energy consumption while meeting a desired tail latency for the request rate. Our work demonstrates three key findings. First, without software changes, energy savings of up to 60% are achievable across diverse hardware systems by controlling batching and processing rates. Second, specialized research Operating Systems (OSes) can leverage this to achieve a further 40% energy savings over general-purpose OSes. Finally, we show that a controller that is agnostic to the application, system, and hardware, can find energy efficient settings for different request rates while meeting performance objectives.