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

TL;DR

This paper presents a black-box approach to optimize energy consumption and latency in network servers by tuning batching and processing rates using Bayesian optimization, achieving significant energy savings without software modifications.

Contribution

It introduces a system-agnostic method to automatically find energy-efficient configurations for latency-sensitive applications without requiring software changes.

Findings

Up to 60% energy savings across hardware systems.

Specialized OSes can be over 2x more energy efficient.

A generic controller can meet SLA while reducing energy use.

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 an SLA target. We find that when the mean offered load is stable, one can find the "sweet spot" settings in packet batching (via interrupt coalescing) and controlling the processing rate (DVFS) that represents optimal trade-offs in the interactions of the software stack and hardware with the arrival rate and composition of requests currently being served. Trying a few combinations of settings on the live system, an example Bayesian optimizer can find settings that reduce the energy consumption to meet a desired tail latency for the current load. This research demonstrates that: 1) without software changes, dramatic energy savings (up to 60%) can be achieved across diverse hardware systems if one controls batching and processing rate, 2) specialized research OSes that have been developed for performance can achieve more than 2x better energy efficiency than general-purpose OSes, and 3) a controller, agnostic to the application and system, can easily find energy-efficient settings for the offered load that meets SLA objectives.