Asynchronous Online Service Placement and Task Offloading for Mobile Edge Computing

Mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading schemes. Many existing works focus on offloading tasks to the edge servers while ignoring the heterogeneity and diversity of computation services, which is also important in MEC. In this paper, we investigate the joint problem of online task offloading and service placement-downloading and deploying the service-related resources at edge servers-in the dense MEC network. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Due to the uncertainty of task demands, it is impossible to make an online long-term optimal decision. Therefore, we propose an online algorithm based on the two-timescale Lyapunov optimization without requiring the future information. By making asynchronous decisions on service placement and task offloading, we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm is more competitive than benchmarks.