Low-Complexity and Efficient Dependent Subtask Offloading Strategy in IoT Integrated With Multi-Access Edge Computing
Haiyan Wang, Wei Li, Jiayu Sun, Li Zhao, Xingmei Wang, Hongwu Lv, Guangsheng Feng
Abstract
Multi-access edge computing (MEC) has been booming in recent years, as it promises to fulfill the growing low-latency requirements of applications on large amounts of Internet of Things (IoT) devices. Nevertheless, as latency-sensitive applications tend to become more complicated, existing schemes are too sophisticated, which may result in exceeding the real-time requirements of IoT systems. In this paper, we investigate the task offloading problem for the multi-device multi-edge server IoT system integrated with MEC. Firstly, according to the performance gains obtained by offloading different subtasks, we formalize a system latency minimization problem with energy utilization consideration, which has been proven to be NP-hard. Then, to address it, we propose a heuristic computation offloading scheduling scheme, which offloads appropriate subtasks to edge servers such that the system latency is minimized. Additionally, we theoretically prove the upper and lower boundaries of the system latency. Extensive simulation results corroborate that the proposed algorithm is low-complexity yet effective in decreasing the system latency by 16.15% (and up to 51.18%), improving the energy efficiency of local devices by 8.97% (and up to 21.69%) and shortening the offloading strategy execution time by 96.33% (and up to 98.95%).