Online Service Deployment on Mega-LEO Satellite Constellations for End-to-End Delay Optimization
Ziqi Li, Heli Zhang, Chunyu Liu, Xi Li, Hong Ji, Victor C. M. Leung
Abstract
Satellite Edge Computing (SEC), which empowers satellites with computing capabilities, has been regarded as a promising paradigm for 6G alongside the development of mega-Low Earth Orbit (LEO) satellite constellations. With SEC, tasks traditionally performed on the ground can be offloaded and processed in the sky. Currently, most existing studies assume that the service entities have been deployed on satellites in advance, ignoring the selection of specific serving nodes. However, as the communication and computing resources of satellites vary in accordance with time, selecting the optimal node for deploying the service entity and migrating it at the right time will significantly affect users' quality of experience. In this regard, this article investigates the online service deployment on mega-LEO satellite constellations, taking into account the time-varying on-board resources and limited visible time. We formulate an optimization problem to maximize the number of successful deployments that meet delay requirements for each user under the long-term migration cost constraint. To solve this problem, we transform it into a Markov decision process and propose COMPOSE, an online satellite service deployment scheme based on convolution-proximal policy optimization. COMPOSE dynamically selects the optimal serving node and migrates the service entity in due course. The simulation results demonstrate the superior performance of COMPOSE in terms of delay satisfaction ratio, delay variance, and the number of migrations.