Throughput-Aware Cooperative Task Offloading in Dynamic Mobile Edge Computing Systems

With the commercialization of fifth-generation (5G) mobile communication technology and the rapid proliferation of mobile devices (MDs), demand for data computation is surging. This growth increases the reliance of MDs on low latency and high throughput. For this purpose, Mobile Edge Computing (MEC) enhances the user's data processing capability by offloading computation tasks to servers at the network edge. However, achieving high efficiency in task offloading is challenging due to factors such as decision complexity, network dynamics, and user data privacy protection. Additionally, energy causal constraints and the coupling between offloading proportions and resource distribution cannot be ignored. In this paper, we first establish a dynamic task offloading problem to optimize the long-term throughput of the system. Using perturbed Lyapunov optimization, we transform MD delay and energy threshold constraints into the stability control of corresponding virtual queues. Then, we propose the Lyapunov-guided federated deep reinforcement learning (DRL) online task offloading algorithm called LyFOTO, which combines a federated learning (FL) framework and an Actor-Critic (AC) model. Under favorable communication conditions, the LyFOTO algorithm adaptively boosts system throughput; under poorer conditions, it properly delays task offloading, without violating queue backlog constraints. Through mathematical analysis, we discuss the performance of the LyFOTO algorithm. Simulation experiments validate that LyFOTO effectively balances system throughput and device battery energy. Finally, Comparative results show that LyFOTO outperforms other benchmark algorithms in maximizing system throughput while ensuring task backlog and energy threshold constraints.