MSs-MEC中基于DRL的服务缓存和任务迁移联合优化算法

DRL-based joint optimization algorithm for service caching and task migration in multi-services mobile edge computing

多服务移动边缘计算(multiple-services mobile edge computing,MSs-MEC)能根据需求自适应调整服务缓存决策,使得部署在用户侧的边缘服务器能够灵活处理不同服务类型的任务。但在实际应用中,特定类型任务的成功迁移依赖于服务环境的提前安装。此外,同时进行任务迁移和服务缓存可能会因时间冲突而导致计算延时。因此,针对上述相关问题,首先将任务迁移和服务缓存决策进行解耦,针对深度强化学习(deep reinforcement learning,DRL)在具有高维的混合决策空间的性能提升不明显的缺点(例如资源分配时利用率不高),将DRL与Transformer结合,通过在历史数据中学习,输出当前时隙的任务迁移决策和下一时隙的任务决策,保证任务到达边缘服务器时能立即执行。其次,为了提高资源分配问题中的资源利用率,将问题分解为连续资源分配问题和离散的任务迁移与服务缓存问题,利用凸优化技术求解资源分配最优决策。广泛的数值结果表明,与其他基线算法相比,提出的算法能有效地减少任务的平均完成时延,同时在资源利用率和稳定性方面也有优异的表现。

MSs-MEC can adaptively adjust service cache decisions according to needs,so that edge servers deploys on the user side can flexibly handle tasks of different service types.However,in practical applications,the successful migration of specific types of tasks depends on the early installation of the service environment.In addition,simultaneous task migration and service caching may result in time conflicts and computation delays.Therefore,in response to the above issues,this paper firstly decoupled task migration and service caching decisions.To address the shortcomings of DRL in high-dimensional mixed decision spaces,where performance improvement was not significant(such as low utilization rate during resource allocation),it combined DRL with Transformer to learn from historical data and output task migration decisions for the current time slot and the next time slot,ensuring that tasks could be executed immediately when they reached the edge server.Secondly,in order to improve resource utilization in resource allocation problems,it decomposed the problem into continuous resource allocation problems and discreted task migration and service caching problems,and used convex optimization technique to solve the optimal decision for resource allocation.Extensive numerical results indicate that the proposed algorithm can effectively reduce the average completion delay of tasks compared to other baseline algorithms,at the same time,it also has excellent performance in resource utilization and stability.