Offloading Cost Optimization in Multiserver Mobile Edge Computing Systems with Energy Harvesting Devices

In mobile edge computing (MEC) systems with energy harvesting, edge devices are powered by unstable energy harvested from the environment. To prolong the lifetime of edge devices, some computing tasks should be offloaded to MEC servers. However, computing services offered by MEC servers may be very costly. In this work, we aim to minimize total costs caused by computing services and dropping tasks while avoiding the devices running out energy. With the consideration of the unpredictability of the harvestable energy, we adopt the stochastic Lyapunov optimization framework to jointly manage energy and make task execution decisions (i.e., local executing, offloading, or dropping tasks) and develop an online algorithm which could help asymptotically obtain the optimal results for the whole system. The algorithm does not require any knowledge of the harvestable energy and the statistics of task arriving processes and can be easily implemented in a distributed manner. Numerical results corroborate that the proposed algorithm can try its best to push battery energy of edge devices to a preset parameter and effectively reduce the service costs and task drops.