Dependent Task Offloading and Resource Allocation in Satellite Edge Computing Networks

With the advent of 6G technology, satellite edge computing networks have emerged as a crucial component of next-generation communication systems. This paper investigates the problem of dependent task offloading in 6G satellite edge computing networks, taking into account the impact of satellite mobility and task dependencies. We formulate an optimization problem to jointly optimize offloading decisions, task waiting times, and computation resource allocation in a multi-satellite and multi-user device scenario. The objective is to minimize the weighted sum of waiting times and energy consumption. To address this problem, we propose a heuristic algorithm to determine the priority of tasks in the queue and develop an improved hybrid genetic grey wolf optimization (HGGWO) algorithm. The HGGWO algorithm effectively solves the optimization problem by leveraging the strengths of both genetic algorithms and grey wolf optimization. Extensive simulations are conducted to evaluate the performance of the proposed approach. The results demonstrate that our algorithm achieves superior performance in terms of total cost compared to four other benchmark schemes.