Abstract
In order to meet people’s demands for intelligent and user-friendly Internet of Things (IoT) services, the amount of computation is increasing rapidly and the requirements of task delay are becoming increasingly more stringent. However, the constrained battery capacity of IoT devices greatly limits the user experience. Energy harvesting technologies enable green energy to provide continuous energy support for devices in the IoT environment. Together with the maturity of the mobile edge computing technology and the development of parallel computing, it provides a strong guarantee for the normal operation of resource-constrained IoT devices. In this article, we design a parallel offloading strategy based on Lyapunov optimization, which is conducive to efficiently finding the optimal decision for delay-sensitive and compute-intensive tasks. We establish a stochastic optimization problem on a discrete-time slot system and propose a green parallel online offloading algorithm (GPOOA). By decoupling the target problem three times, the joint optimization of green energy, task division factor, CPU frequency, and transmission power is realized. Experimental results demonstrate that under the constraints of strict task deadlines and limited server computing resources, GPOOA performs well in terms of system cost and task drop ratio, far superior to several existing offloading algorithms.
