移动边缘计算中多约束下的任务卸载和资源分配算法

A task offloading and resource allocation algorithm under multiple constraints in mobile edge computing

随着物联网和车载网的普及与应用,近用户端(数据源端)的数据呈现爆炸式的增长。为了有效地处理这些快速增长的数据,移动边缘计算作为一种新的计算模式应运而生。移动边缘计算是指将云中心的部分资源下沉到网络边缘,使得数据能够在网络边缘被处理。如何高效地卸载任务以及合理地分配资源,是目前移动边缘计算研究领域中的一个热点问题;然而现有的研究工作很少考虑到边缘数据和计算节点的安全性,只有保证数据与信息的安全,移动边缘计算才能全面发展。因此,基于数据的安全性,结合深度强化学习在多约束条件下提出了一种任务卸载和资源分配算法。实验结果表明,该任务卸载算法与几种经典算法相比,有效地提高了任务卸载成功率、任务成功执行率,降低了本地端能耗,更好地满足了用户的QoS需求。

With the popularization and application of the Internet of Things and the vehicle network,the data at the near user end(data source end)has shown an explosive growth.To effectively deal with these rapidly growing data,mobile edge computing has emerged as a new computing model.Mobile edge computing refers to sinking some resources in the cloud center to the edge of the network,so that data can be processed at the edge of the network.How to efficiently offload tasks and allocate resources reasonably is a hot issue in the field of mobile edge computing research.However,most of the existing studies ignore the security of edge data and computing nodes and only guarantees the security of data and information,and the mobile edge computing can develop comprehensively.Therefore,based on data security,combined with deep reinforcement learning,a task offloading and resource allocation algorithm is proposed under multiple constraints.Experimental results show that,compared with several classic algorithms,the algorithm can effectively improve the task offloading success rate and task successful execution rate,reduces the local energy consumption,and better meets the user's QoS requirements.