Computational Task offloading Algorithms for Multi-edge Node Scenarios

In this study, we propose a multi-edge and multi-equipment unloading model based on an improved frog leaping algorithm in order to solve the issues of resource waste, low efficiency, and high energy consumption caused by idle remote edge server equipment in a multi-edge unloading environment. For this, the location of terminal equipment is randomly generated, and the list of its uninstallable servers is examined. The quality of the unloading decision is examined using the objective function, which includes the weighted sum of delay and energy consumption. In addition, the standard frog leaping algorithm is improved to meet the needs of the calculation task unloading model. After the addition of the adaptive weight, the position of the frog individual is updated based on XOR (Exclusive OR) operation. Moreover, the concept of mutation in the genetic algorithm is also introduced in this study. Finally, the performance of the proposed unloading algorithm is compared with that of four other mainstream unloading algorithms. Our simulation results show that the proposed unloading scheme obtains better unloading decisions, better objective function value instant delay and better energy consumption optimization than the other algorithms.