A Distributed Stochastic Task Offloading Methodology for IoT on e-Health
Shichao Xia, Zhixiu Yao, Yun Li · 2020
With the rapid development of Internet of Things (IoT) on e-Health, the role of Mobile Edge Computing (MEC) has been increasingly effective in providing high-performance, lowlatency computing services. In this work, we consider the problem of task offloading and computing resource allocation in dynamic environment, wherein heterogeneous IoT devices or e-Health applications with diverse requirements in latency and energy constraint. Taking into account the different traffic characteristics and spatio-temporally varying distributed environment, we formulate the offloading problem as a dynamic game and a Stackelberg Equilibrium (SE) based distributed online offloading manner is proposed. And then, to allocate computing resource on demand, a dynamic quote price mechanism is designed by invoking Lyapunov optimization. Furthermore, to improve processing efficiency and reduce unnecessary communication overhead, a “first-rank” servers selection criteria is proposed by balancing revenue and latency. Finally, the effectiveness and rationality of the algorithm are verified by experimental simulation.