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

TL;DR

This paper proposes an online framework combining optimization and deep reinforcement learning to enhance low-latency and data freshness in mobile edge computing networks, addressing the joint challenges of caching, offloading, and resource allocation.

Contribution

It introduces a novel Lyapunov-based online approach with an integrated optimization-DRL method for joint service caching, task offloading, and resource management in MEC networks.

Findings

OIODRL achieves near-optimal solutions in simulations.

The method outperforms benchmark algorithms.

It effectively balances latency and data freshness.

Abstract

Edge service caching can significantly mitigate latency and reduce communication and computing overhead by fetching and initializing services (applications) 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 without knowledge of future system information, which is formulated as a joint online long-term optimization problem. This problem is NP-hard. To solve the 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 contains an optimization stage including a quadratically constrained quadratic program (QCQP) transformation and a semidefinite relaxation (SDR) method, and a learning stage including a deep reinforcement learning (DRL) algorithm. Extensive simulations show that the proposed OIODRL method achieves a near-optimal solution and outperforms other benchmark methods.