An online energy-saving offloading algorithm in mobile edge computing with Lyapunov optimization

Online computing offloading is an effective method to enhance the performance of mobile edge computing (MEC). However, existing research ignores the impact of system stability and device priority on system performance during task processing.To address the problem of computing offloading for computing-intensive tasks, an online partial offloading algorithm combining task queue length and energy consumption is proposed without any prior information. Firstly, a queue model of IoT devices is created to describe their workload backlogs and reflect the system stability. Then, using Lyapunov optimization, computing offloading problem is decoupled into two sub-problems by calculating the optimal CPU computing rate and device priority, which can determine the task offloading amount and offloading location to complete resource allocation. Finally, the online partial offloading algorithm based on devices priority is solved by minimizing the value of the drift-plus-penalty function’s upper bound to ensure system stability and reduce energy consumption. Theoretical analysis and the outcomes of numerous experiments demonstrate the effectiveness of the proposed algorithm in minimizing system energy consumption while adhering to system constraints, even in dealing with dynamically varying task arrival rates.