移动边缘计算中计算卸载与资源分配联合优化策略

A joint optimization strategy for compute offloading and resource allocation in mobile edge computing

为了在移动边缘计算(MEC)中最大限度地减少处理用户任务的时延和能耗,改善用户体验,以最小化用户的完成时间和能耗的加权和为目标,在计算资源的约束下研究了多用户、多MEC服务器中的计算卸载问题。针对此问题,考虑卸载决策和资源分配之间存在的依赖关系,首先将原问题解耦为卸载决策和计算资源分配2个子问题。然后,使用鲸鱼优化算法求解卸载决策问题,通过添加非线性收敛因子和惯性权重加快收敛速度;引入反馈机制,防止陷入局部最优,得到更高概率可行的卸载决策;对于资源分配问题使用拉格朗日乘子法得到每个卸载决策下的最佳计算资源分配解。最后,通过多次迭代得到稳定的收敛解。仿真实验结果表明,与其他基准方案相比,最多减少了44.6%的系统开销。

In order to minimize the processing latency and energy consumption for user tasks in Mobile Edge Computing(MEC)and enhance user experience,this paper focuses on the computation offloading problem in a multi-user,multi-MEC server scenario under constraints on computational resources.With the objective of minimizing the weighted sum of user completion time and energy consumption,the problem is tackled by first decoupling it into two sub-problems:offloading decision and computation resource allocation.The Whale Optimization Algorithm is employed to solve the offloading decision problem,enhancing convergence speed by introducing a nonlinear convergence factor and inertial weight.A feedback mechanism is introduced to prevent local optima,yielding offloading decisions with higher probability of feasibility.The resource allocation problem is addressed using the Lagrange multiplier method to obtain the optimal computation resource allocation for each offloading decision.Finally,stable converged solutions are obtained through multiple iterations.Simulation results demonstrate that,compared to other benchmark solutions,the proposed approach reduces the system overhead by up to 44.6%.