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

2025-02-20

AI Summary

This work addresses energy optimization for latency-sensitive network services under tail-latency SLA constraints. We propose a black-box, online co-tuning method that jointly optimizes interrupt coalescing (packet batching) and Dynamic Voltage and Frequency Scaling (DVFS), requiring no modifications to applications or the OS kernel. Our approach employs a generic, application- and system-agnostic Bayesian optimization controller that rapidly converges to the optimal energy-efficiency operating point under SLA constraints using minimal online probing. Key contributions include: (i) achieving up to 60% energy reduction while meeting tail-latency SLAs; (ii) revealing that specialized OS kernels improve energy efficiency by over 2× compared to general-purpose kernels; and (iii) demonstrating strong generalizability and stability across diverse hardware platforms. The method enables practical, deployment-ready energy-aware tuning for high-performance network services without sacrificing latency guarantees.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Optimize energy-performance trade-offs
• Control batching and processing rate
• Achieve SLA targets efficiently

Innovation

• Bayesian optimizer
• interrupt coalescing
• DVFS control