LAUSR

Driven by the demands of diverse artificial intelligence (AI)-enabled application, Mobile Edge Computing (MEC) is considered one of the key technologies for 6G edge intelligence. In this paper, we consider a serial task model and design a quality of service (QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems, which can mitigate the I/O interference brought by resource reuse among virtual machines. Then we construct the system utility measuring QoS based on application latency and user devices' energy consumption. We also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O interference. Simulation results demonstrate the proposed algorithm's significant advantages in terms of task completion time, terminal energy consumption and system resource utilization.