Mobile Edge Computing Networks: Online Low-Latency and Fresh Service Provisioning

Edge service caching can significantly mitigate latency and reduce communication and computing overhead by downloading and caching service (application) data from clouds. The freshness of cached service data is critical when providing satisfactory services to users, but has been overlooked in existing research efforts. In this paper, we study the online low-latency and fresh service provisioning in mobile edge computing (MEC) networks. Specifically, we jointly optimize the service caching, task offloading, and resource allocation. To solve the formulated joint online long-term optimization problem, we design a Lyapunov-based online framework that decouples the problem at temporal level into a series of per-time-slot subproblems. For each subproblem, we propose an online integrated optimization-deep reinforcement learning (OIODRL) method, which consists of an optimization stage and a learning stage. In the optimization stage of OIODRL, a quadratically constrained quadratic program (QCQP) transformation and a semidefinite relaxation (SDR) method are utilized; in the learning stage of OIODRL, a deep reinforcement learning (DRL) algorithm is applied. Extensive simulations show that the proposed OIODRL method achieves a near-optimal solution and outperforms benchmark methods.