An Improved Chaotic Bat Swarm Scheduling Learning Model on Edge Computing

Abstract

Edge computing has strong real-time and big data interaction processing requirements. The long scheduling time and load imbalance among edge nodes and edge servers are the key problems of edge computing. The current cloud computing scheduling algorithms all have balance problems between algorithm complexity and performance, and cannot fundamentally solve the contradiction. It is a feasible method to use the deep learning model to train the scheduled data to achieve a direct prediction of the scheduling results. This paper mainly studies from two aspects, one is to obtain more accurate training data from the perspective of researching optimal scheduling algorithms, and the other is to improve the training speed from the perspective of improving the deep learning model. At first, an improved chaotic bat swarm algorithm is put forward. It introduces chaotic factors and second-order oscillation mechanisms to improve the speed of update and dynamic parameter mechanisms. Subsequently, the long short-term memory network deep learning model is trained with the historical data by the improved algorithm. The experimental results show that the improved learning model can achieve the purpose of quickly predicting the scheduling result.