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Mobile Edge Computing (MEC) is a distributed computing paradigm that provides computing capabilities at the periphery of mobile cellular networks. This architecture empowers Mobile Users (MUs) to offload computation-intensive applications to large-scale computing nodes near the edge side, reducing application latency for MUs. The resource allocation and task offloading in MEC has been widely studied. However, the burgeoning complexity inherent to modern applications, often represented as Directed Acyclic Graphs (DAGs) comprising a multitude of subtasks with interdependencies, poses huge challenges for application offloading and resource allocation. Meanwhile, previous work has neglected the impact of edge caching on the offloading execution of dependent tasks. Therefore, this paper introduces a novel dynamic caching dependency-aware task offloading (CachOf) scheme. First, to effectively enhance the rationality of cache and computing resource allocation, we develop a subtask priority computation scheme based on DAG dependencies. This scheme includes the execution sequence priority of subtasks on a single MU and the offloading sequence priority of subtasks from multiple MUs. Second, a dynamic caching scheme, designed to cater to dependent tasks, is proposed. This caching approach can not only assist offloading decisions, but also contribute to load balancing by harmonizing caching resources among edge servers. Finally, based on the task prioritization results and caching results, this paper presents a Deep Reinforcement Learning (DRL)-based offloading scheme to judiciously allocate resources and improve the execution efficiency of applications. Extensive simulation experiments demonstrate that CachOf outperforms other baseline schemes, achieving improved execution efficiency for applications.
Zhao et al. (Thu,) studied this question.