Winning at the Starting Line: Joint Network Selection and Service Placement for Mobile Edge Computing

Abstract

Mobile Edge Computing (MEC) is an emerging computing paradigm in which computational capabilities are pushed from the central cloud to the network edges. However, preserving the satisfactory quality-of-service (QoS) for user applications is non-trivial among multiple densely dispersed yet capacity constrained MEC nodes. This is mainly because both the access network and edge nodes are vulnerable to network congestion. Previous works are mostly limited to optimizing the QoS through dynamic service placement, while ignoring the critical effects of access network selection on the network congestion. In this paper, we study the problem of jointly optimizing the access network selection and service placement for MEC, towards the goal of improving the QoS by balancing the access, switching and communication delay. Specifically, we first design an efficient online framework to decompose the long-term optimization problem into a series of one-shot problems. To address the NP-hardness of the one-shot problem, we further propose an iteration-based algorithm to derive a computation efficient solution. Both rigorous theoretical analysis on the optimality gap and extensive trace-driven simulations validate the efficacy of our proposed solution.