Energy allocation and task scheduling in edge devices based on forecast solar energy with meteorological information

Abstract

Offloading tasks from edge devices to the cloud is an important method to enhance the performance of the edge device. With the help of EH (Energy Harvesting) technology, the edge device can use the collected green energy to support its operations. Most offloading scheduling methods use as much green energy as the edge device collected. Unlike prior research, we consider the long-term benefits of energy. In this paper, we forecast the solar energy supply with meteorological methods which are based on the weather forecast data. Then, we use quadratic programming to allocate energy based on the forecast energy to maximize energy efficiency. Finally, we use NSGA (Non-dominated Sorting Genetic Algorithms) to offload tasks in the edge device. Simulations show that our proposed method not only minimizes the execution time and the energy consumption of clouds, but also enhances the QoE of users.