Learn to Coordinate for Computation Offloading and Resource Allocation in Edge Computing: A Rational-Based Distributed Approach

Abstract

In response to the growing demand for user experience on innovative applications, computation offloading can migrate computation-intensive tasks from users to the network edge, which is closely coupled with resource allocation. For the rational motivations behind these issues, centralized decision-making may seriously compromise individuals’ rationality, and the complexity of this problem is gradually beyond the comfort zone of traditional methods. Therefore, realizing decentralized and intelligent resource management becomes an emerging technical issue in edge computing, which has not been much explored. This paper proposes a learning-based distributed resource coordination framework, L6C, which enables each decision-maker to pursue its benefit in a socializing and rational manner. We first formulate a two-timescale problem of computation offloading and resource allocation, then exploit game theory to discuss the rational properties of users and edges. Specifically, each user wants to minimize the execution cost of its task, and edges try to maximize the experience of task execution cooperatively. Further, we design two explicit information interaction mechanisms based on multi-agent deep reinforcement learning, where interactive contents can be generated dynamically along with resource decisions. Experimental results on a real-world dataset show that L6C achieves the superior performance of edges and users compared with various baselines.