Joint Network Selection and Task Offloading in Mobile Edge Computing

As some delay-sensitive mobile services such as augmented reality and autonomous driving proliferate, users' demand for low latency access to computation resources increases dramatically, and existing centralized cloud computing paradigm is difficult to solve the current dilemma. As a emerging computing paradigm in which computational capabilities are pushed from the central cloud to the network edges, Mobile Edge Computing (MEC) is expected to be an effective solution. However, due to the limited capacity (e.g. computation and bandwidth) of MEC nodes, it is not easy to maintain satisfactory quality of service for user applications. Most of the previous work is limited to reducing the processing delay by dynamically adjusting the task offloading strategy, while ignoring the key impact of access network selection on network congestion. To fill this gap, we study the joint optimization of network selection and task offloading in MEC networks with multidimensional resources constraints. To address a number of key challenges in MEC systems, including spatial demand coupling and decentralized coordination, we propose an efficient online algorithm and achieve provable close-to-optimal performance. Extensive simulation results are presented to verify the performance of our algorithm.