Joint Task Offloading and Channel Allocation in Spatial-Temporal Dynamic for MEC Networks

TL;DR

This paper proposes a joint task offloading and channel allocation framework for MEC networks that dynamically adapts to spatial-temporal variations using deep reinforcement learning, optimizing delay-energy trade-offs.

Contribution

It introduces a novel joint optimization approach combining a priority scheme, a grouped Knapsack model, and a D3QN-based decision method for MEC resource management.

Findings

Significant reduction in delay-energy trade-off costs.

Enhanced adaptability to dynamic spatial-temporal MEC environments.

Effective handling of task dependencies and resource competition.

Abstract

Computation offloading and resource allocation are critical in mobile edge computing (MEC) systems to handle the massive and complex requirements of applications restricted by limited resources. In a multi-user multi-server MEC network, the mobility of terminals causes computing requests to be dynamically distributed in space. At the same time, the non-negligible dependencies among tasks in some specific applications impose temporal correlation constraints on the solution as well, leading the time-adjacent tasks to experience varying resource availability and competition from parallel counterparts. To address such dynamic spatial-temporal characteristics as a challenge in the allocation of communication and computation resources, we formulate a long-term delay-energy trade-off cost minimization problem in the view of jointly optimizing task offloading and resource allocation. We begin by designing a priority evaluation scheme to decouple task dependencies and then develop a grouped Knapsack problem for channel allocation considering the current data load and channel status. Afterward, in order to meet the rapid response needs of MEC systems, we exploit the double duel deep Q network (D3QN) to make offloading decisions and integrate channel allocation results into the reward as part of the dynamic environment feedback in D3QN, constituting the joint optimization of task offloading and channel allocation. Finally, comprehensive simulations demonstrate the performance of the proposed algorithm in the delay-energy trade-off cost and its adaptability for various applications.