A Trade-Off Task-Offloading Scheme in Multi-User Multi-Task Mobile Edge Computing

Mobile edge computing (MEC) is a novel technique that can reduce computational burden of local terminal devices by tasks offloading, which emerges as a promising paradigm to provide computing capabilities near mobile users. Furthermore, the computing power consumption and battery capacity in the terminal devices is generally limited in MEC, which directly affect the quality of service (QoS), which brings new challenge in achieving the stability of the system. The trade-off between delay and energy consumption of task offloading process under multi-user MEC scenario is in general a complicated problem that highly correlates to the computation offloading decisions of computation tasks, i.e., whether or not to offload a task for edge execution. To address the above issues, the task offloading decision to solve the queuing problem of tasks to be processed in the local terminals was being considered, with an application of Lyapunov theory to ensure the queue stability in this paper. Then, a trade-off model was formulated to minimize the delay and energy consumption to achieve a minimum execution cost. Moreover, an improved Dynamic Niche-based Self-organizing Learning Algorithm was presented to accelerate the speed of the search process to gain an optimal task offloading scheme. The simulation results provided supporting evidence that the proposed optimal scheme IDO could achieve a lower average energy consumption than LFO and a lower average execution delay than EFO under the scenarios of the waiting queue length varies within [10, 50], the number of users varies within [4, 20], and the number of tasks varies within [20, 100].