An Adaptive Slicing-Based Task Admission Scheduling Strategy in Multiaccess Edge Computing

The rise of multiaccess edge computing (MEC) speeds up mobile user services and resolves service delays caused by long-distance transmission to cloud servers. However, in task-intensive scenarios, edge server processing limitations lead to buffer congestion, increasing latency and reducing Quality of Service (QoS). Furthermore, the challenges of edge server task processing are increased by the varying deadline requirements of different tasks, the time variability of task arrivals, and the real-time fluctuations of the network. In this work, we propose an adaptive slicing-based task admission scheduling strategy (ASTA) to address these issues. ASTA consists of an adaptive time slice adjustment algorithm (ASTA-I) and a task admission scheduling algorithm (ASTA-II). ASTA-I dynamically adjusts time slices based on real-time network conditions and task flow. ASTA-II first adjusts task priorities dynamically by considering factors, such as data volume, deadlines, network conditions, and buffer locations. After that, ASTA-II formulates different scheduling strategies based on changes in task priorities. These strategies are formulated to improve the throughput efficiency of edge servers and enhance the average response speed of tasks. Simulation results show that compared with the existing O2A and OTDS in different scenarios, the proposed ASTA can reduce the average number of waiting requests in the edge server buffer by 19.53%–57.73% and 20.42%–50.26%, and accelerates the average response speed of tasks by about 39.76% and 32.41%.