Joint Task Offloading and Data Caching in Mobile Edge Computing

Mobile Edge Computing (MEC) has recently become an important paradigm of bringing computing and caching resources to the edge networks. In mobile edge networks, data content can be cached in MEC servers to efficiently improve the performance of mobile users' service by sharing data among MEC servers rather than sending data requests to remote content server. However, the resource of edge network is relatively limited, and only a small amount of application data can be cached in edge server. Thus, it is necessary to figure out the wise caching decision to minimize edge computing latency. In this paper, we consider the scenario where multiple mobile users offload duplicate tasks to the edge network and share the data required for computing tasks. We design a joint computation offloading and data caching model to minimize the overall execution latency for all mobile users. Moreover, we propose an efficient online algorithm based on Lyapunov optimization, which jointly schedules computation offloading and allocates data caching for computation/data requests sent by mobile users. The simulation results show that the algorithm can effectively reduce the computation delay of end users while maintaining low energy consumption.