Dynamic Task Caching and Computation Offloading for Mobile Edge Computing

Mobile edge computing (MEC) provides information technology and cloud-computing capabilities within the network edge close to mobile users, thereby addressing computing demand for users. However, the energy consumption of data uploading from users to the MEC server makes it hard to meet users' demand in some specific applications, i.e., interactive gaming and augmented reality. Motivated by this, we integrate a task caching mechanism into computation offloading technique. Specifically, it allows the MEC server to proactively cache some tasks and users to offload their tasks to the MEC server. Since the limited storage capacity and task demands for users are changing dynamically, which tasks are cached has to be judiciously decided to maximize the MEC system performance. The objective of this paper is to minimize the system cost, which is defined as the average total user energy consumption of all time slots. By formulating the problem as an integer-programming, we propose to find the optimal solution with two steps. Through which we have obtained the optimal online computation offloading and task cache update strategy. Simulation results show that in comparison with the other two baselines, the proposed scheme can effectively reduce the system cost.