Cache-Assisted Offloading Optimization for Edge Computing Tasks

Mobile edge computing (MEC) serves as a feasible architecture that brings computation closer to the edge, enabling rapid response to user demands. However, most research on task offloading (TO) overlooks the scenario of repetitive requests for the same computing tasks during long time slots, and the spatiotemporal disparities in user demands. To address this gap, in this paper, we first introduce edge caching into TO and then divide base stations (BSs) into different communities based on the regional characteristics of user demands and activity areas, enabling collaborative caching among BSs within the same community. Subsequently, we design a dual timescale to update task popularity within both short and long-term time slots. To maximize cache benefits, we construct a model that transforms the caching issue into a 0–1 knapsack problem, and employ dynamic programming to obtain offloading strategies. Simulation results confirm the efficiency of the proposed task caching policy algorithm, and it effectively reduces the offloading cost and improves cache resource utilization compared to the other three baseline algorithms.In this paper, we first introduce edge caching into TO and then divide BSs into different communities based on the regional characteristics of user demands and activity areas, enabling collaborative caching among BSs within the same community. Subsequently, we design a dual timescale to update task popularity within both short and long-term time slots. To maximize cache benefits, we construct a model that transforms the caching issue into a 0–1 knapsack problem and employ dynamic programming to obtain offloading strategies.