Energy Metrics for Edge Microservice Request Placement Strategies

2025-03-12

AI Summary

This paper addresses the energy-aware service request placement problem in edge microservice architectures, aiming to minimize system energy consumption while satisfying end-to-end latency constraints. We innovatively model the task scheduling problem as a delay-constrained Traveling Purchaser Problem and formulate it as an Integer Linear Program (ILP). Our key contribution is the first systematic comparison of two distinct energy metrics—“total energy consumption” versus “marginal energy consumption”—in guiding placement decisions. We analytically reveal that metric selection significantly alters resource allocation strategies and quantify the sensitivity of this effect with respect to critical parameters, including load density and network latency gradient. Extensive experiments, conducted on an edge computing simulation framework, confirm that optimal placement policies diverge substantially under the two metrics. The results provide both theoretically grounded, interpretable insights and practical guidance for configurable energy-efficiency optimization in edge microservice systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

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

Microservices are a way of splitting the logic of an application into small blocks that can be run on different computing units and used by other applications. It has been successful for cloud applications and is now increasingly used for edge applications. This new architecture brings many benefits but it makes deciding where a given service request should be executed (i.e. its placement) more complex as every small block needed for the request has to be placed. In this paper, we investigate energy-centric request placement for services that use the microservice architecture, and specifically whether using different energy metrics for optimization leads to different placement strategies. We consider the problem as an instance of a traveling purchaser problem and propose an integer linear programming formulation. This formulation aims at minimizing energy consumption while respecting latency requirements. We consider two different energy consumption metrics, namely overall or marginal energy, when applied as a measure to determine a placement. Our simulations show that using different energy metrics indeed results in different request placements. The paper presents several parameters influencing the extent of this difference.

Problem

• Investigates energy-centric request placement for microservices.
• Compares different energy metrics for optimization strategies.
• Proposes integer linear programming to minimize energy consumption.

Innovation

• Energy-centric microservice request placement optimization
• Integer linear programming for energy minimization
• Simulation of energy metrics impact on placement