Collaborative Offloading Strategy for Dependent Tasks in Mobile Edge Computing

Mobile edge computing offloads computing-intensive applications from resource-constrained terminal devices to adjacent edge servers to meet users’ latency and energy consumption requirements. Most existing studies do not consider the dependencies between applications, leading to the wastage of computing resources. As the number of request users increases, edge servers with limited resources cannot meet the needs of all users. However, there are a large number of idle computing resources on the user side that are not utilized. Aiming at the problem of computing offloading of dependent tasks in this scenario, we establish an end-edge collaboration-dependent task offloading model and propose an offloading algorithm that balances task completion time and energy consumption. Firstly, we solve the problem of collaboratively matching request users by considering user mobility and computing requirements. Secondly, we determine the scheduling order of tasks according to the dependencies between tasks. Finally, we propose a hybrid artificial bee colony algorithm to solve the problem of task offloading. The results show that our algorithm saves 19.9% in average task completion time compared to an offloading strategy that does not consider device-to-device.