An Online Framework for Joint Network Selection and Service Placement in Mobile Edge Computing

With the rapid development and deployment of 5G wireless technology, mobile edge computing (MEC) has emerged as a new computing paradigm to facilitate a large variety of infrastructures at the network edge to reduce user-perceived communication delay. One of the fundamental problems in this new paradigm is to preserve satisfactory quality-of-service (QoS) for mobile users in light of densely dispersed wireless communication environment and often capacity-constrained MEC nodes. Such user-perceived QoS, typically in terms of the end-to-end delay, is highly vulnerable to both access network bottleneck and communication delay. Previous works have primarily focused on optimizing the communication delay through dynamic service placement, while ignoring the critical effect of access network selection on the access delay. In this work, we study the problem of jointly optimizing the access network selection and service placement for MEC, with the objective of improving the QoS in a cost-efficient manner by judiciously balancing the access delay, communication delay, and service switching cost. Specifically, we propose an efficient online framework to decompose a long-term time-varying optimization problem into a series of one-shot subproblems. To address the NP-hardness of the one-shot problem, we design a computationally-efficient two-phase algorithm based on matching and game theory, which achieves a near-optimal solution. Both rigorous theoretical analysis on the optimality gap and extensive trace-driven simulations are conducted to validate the efficacy of our proposed solution.