Energy-Efficient Joint Offloading and Resource Allocation for Deadline-Constrained Tasks in Multi-Access Edge Computing

2025-09-14

AI Summary

This paper addresses energy-efficient task offloading for deadline-constrained IoT applications in multi-access edge computing (MEC), aiming to maximize total energy savings across end devices through joint offloading decision-making and communication-computation resource allocation. Method: We propose a general offloading model supporting computation offloading to non-co-located edge servers and formulate the problem as an integer nonlinear program (INLP). To solve it efficiently, we design the first approximation algorithm—Graph-based Matching Algorithm (GMA)—based on tripartite graph matching, supported by linear programming relaxation, graph-theoretic modeling, and LP rounding. Contribution/Results: GMA is theoretically proven to achieve a $(1-alpha)/(2+varepsilon)$ approximation ratio. Experiments demonstrate that GMA attains, on average, 97% of the optimal energy savings, significantly outperforming baseline methods. It constitutes the first provably efficient, theoretically guaranteed energy-saving solution for latency-sensitive MEC offloading.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Mixed Discrete/Continuous Search
• 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
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper addresses the deadline-constrained task offloading and resource allocation problem in multi-access edge computing. We aim to determine where each task is offloaded and processed, as well as corresponding communication and computation resource allocations, to maximize the total saved energy for IoT devices, while considering task deadline and system resource constraints. Especially, our system allows each task to be offloaded to one of its accessible access points (APs) and processed on a server that is not co-located with its offloading AP. We formulate this problem as an Integer Nonlinear Programming problem and show it is NP-Hard. To address this problem, we propose a Graph-Matching-based Approximation Algorithm ($mathtt{GMA}$), the first approximation algorithm of its kind. $mathtt{GMA}$ leverages linear relaxation, tripartite graph construction, and a Linear Programming rounding technique. We prove that $mathtt{GMA}$ is a $frac{1-α}{2+ε}$-approximation algorithm, where $ε$ is a small positive value, and $α$ ($0$$le$$α$$<$$1$) is a system parameter that ensures the resource allocated to any task by an AP or a server cannot exceed $α$ times its resource capacity. Experiments show that, in practice, $mathtt{GMA}$'s energy saving achieves $97%$ of the optimal value on average.

Problem

• Optimizing energy-efficient task offloading in edge computing
• Allocating communication and computation resources under deadlines
• Maximizing IoT device energy savings with resource constraints

Innovation

• Graph-Matching-based Approximation Algorithm for NP-Hard problem
• Linear relaxation and tripartite graph construction technique
• LP rounding method for energy-efficient resource allocation