Alternating optimization for energy consumption-oriented task offloading in SAGIN

As a fundamental framework for future mobile communication systems, the Space-Air-Ground Integrated Network (SAGIN) leverages Multi-Access Edge Computing (MEC) technology to provide communication and computation resources to users by connecting devices at different network levels. However, current task offloading schemes often suffer from suboptimal energy consumption performance and fail to balance various constraints, such as communication resources, computation resources, maximum allowable task delay, and the maximum coverage time of Low Earth orbit (LEO) satellites. To address these issues, this paper focuses on the energy consumption-driven task offloading problem within SAGIN. Our approach differs from existing models by explicitly considering the aforementioned constraints in the task allocation process. To tackle the complexity of the original problem more efficiently and to provide a structured way to address both the communication and computation processes, the problem is formulated into two subproblems: the bandwidth allocation problem and the MEC task offloading decision problem. To solve these subproblems, we propose the Joint Bandwidth Allocation and MEC Task Offloading decision-alternating Optimization (JBAMTO-AO) algorithm. The CVX toolkit and the alternating direction method of multipliers (ADMM) distributed algorithm are employed to address the decomposed subproblems effectively. Extensive experimental evaluations show that the proposed JBAMTO-AO algorithm outperforms existing offloading methods in terms of both the total energy consumption of system tasks and the satisfaction degree of task maximum tolerable delay.