Satellite Edge Computing With Collaborative Computation Offloading: An Intelligent Deep Deterministic Policy Gradient Approach

Abstract

Enabling a satellite network with edge computing capabilities can complement the advantages further of a single terrestrial network and provide users with a full range of computing service. Satellite edge computing is a potentially indispensable technology for the future satellite-terrestrial integrated networks. In this paper, a three-tier edge computing architecture consisting of terminal-satellite-cloud is proposed, where tasks can be processed at three planes and inter-satellites can cooperate to achieve on-board load balancing. Facing varying and random task queues with different service requirements, we formulate the objective problem of minimizing the system energy consumption under the delay and resource constraints, and jointly optimize the offloading decision, communication and computing resource allocation variables. Moreover, the distribution of resources is based on the reservation mechanism to ensure the stability of satellite-terrestrial link and the reliability of computation process. To adapt to the dynamic environment, we propose an intelligent computation offloading scheme based on the deep deterministic policy gradient (DDPG) algorithm, which consists of several different deep neural networks (DNN) to output both discrete and continuous variables. Additionally, by setting the selection process of legal actions, the simultaneous decisions on offloading locations and allocating resources under multi-task concurrency is realized. The simulation results show that the proposed scheme can effectively reduce the total energy consumption of the system by ensuring that the task is completed on demand, and outperform the benchmark algorithms.