Attention Cooperative Task Offloading and Service Caching in Edge Computing

Abstract

Mobile edge computing (MEC) enables various services to be cached in close proximity to the user equipments (UEs), thereby reducing the computing delay of many emerging applications. Nevertheless, The limited storage capacity of edge servers requires judicious design of service caching as well as task offloading to maximize edge computing performances. In this paper, we formulate a cooperative task offloading, service caching, and transmit power allocation problem to minimize the cost of computing delay and energy consumption of UEs. To address this problem, we propose a graph attention based multi-agent deep deterministic policy gradient (GAT-MADDPG) algorithm, in which a multi-headed graph attention mechanism is incorporated into the centralized critic network to learn the attentive cooperation policies. Simulation results show that the proposed GAT-MADDPG algorithm exhibits an effective performance improvement.