부산대학교 도서관

The Industrial Internet of Things (IIoT) integrates diverse wireless and heterogeneous devices to enable time-sensitive applications. Multi-access edge computing (MEC) offers computing services for nearby tasks to meet their time requirements. However, offloading a large number of tasks to servers with minimal time is a challenging issue. Existing approaches typically allocate tasks into equal-length timeslots for offloading based on optimization or heuristic methods, overlooking the time-varying nature of task arrival density. This neglect significantly increases task execution time. To address this problem, we propose an Adaptive Task Offloading scheme with two-stage hybrid Matching (ATOM). In ATOM, a global buffer with an adjustable threshold is employed to store task information, enabling it to adapt to the time-varying arrival density and execute different offloading stages accordingly. In the online matching stage, if the threshold is not reached, tasks in the buffer are promptly offloaded to the most suitable server. In the offline matching stage, when the threshold is exceeded, all tasks in the buffer are optimally matched with servers and offloaded in batches. Experimental results demonstrate that ATOM outperforms state-of-the-art schemes in terms of average execution time and timeout rate, achieving reductions of 23.3% and 10.4%, respectively.