Joint scheduling and offloading of computational tasks with time dependency under edge computing networks

Mobile edge computing is an emerging technology paradigm that improves mobile devices’ computational power and efficiency by offloading computational tasks to edge servers. However, there are still some problems: (1) Ignoring the complex time dependencies of compute-intensive or latency-sensitive tasks, leading to the omission of offload scheduling opportunities for fine-grained sub-tasks within a task, thus reducing the computational power of the mobile device and the utilization of edge resources; (2) Overly biased towards optimizing task execution latency and ignoring the balance between data transmission latency and energy loss, reducing the scheme versatility in natural computing environments. Aiming at the above problems, this paper models the complex dependencies, transmission time, and execution time between computing tasks, constructs a mathematical model of the scheduling and offloading process of computing tasks, and a joint task scheduling and offloading method (N-JSU) based on Nash equilibrium is designed. This offloading strategy is divided into two steps: first, the average latency of each task is analyzed, and the scheduling order of the computational tasks is determined based on the highest response ratio of the tasks; secondly, the optimal solution is determined by iterative search to offload the most suitable IoT tasks or computing tasks to the edge server. The experimental results show that this offloading strategy can effectively reduce the latency of computationally intensive edge network tasks and improve the resource utilization of the edge server.