Potential Game-Based Computation Offloading in Edge Computing With Heterogeneous Edge Servers

With the proliferation of mobile phones, IoT devices, and the rising demand for computational resources, computation offloading has emerged as a promising technique for improving performance, and optimizing resource usage. It involves transferring computational tasks from local devices to edge servers. However, reducing latency and device energy consumption remains a challenge in current research. In this paper, we propose a potential game-theoretic approach to optimize computation offloading in edge computing environments. We consider heterogeneous edge servers, where each server may have different computational capabilities. By formulating the problem as a potential game, we have end devices acting as players deciding whether to execute tasks locally or on edge servers. Our framework includes utility functions capturing the latency-energy consumption trade-off. Through a detailed analysis, we introduce an innovative algorithm for potential games aiming at achieving Nash equilibrium. This algorithm demonstrates exceptional convergence properties, ensuring reliable convergence even in complex scenarios. Extensive experiments validate the convergence of our algorithm and demonstrate its better performance compared to other benchmark algorithms in terms of latency and energy consumption.