Research on Load Prediction Based on Improve GWO and ELM in Cloud Computing
Shengcai Zhang, Dezhi An, Zhenxiang He · 2019
In order to promote the accuracy and stability of load short-term prediction, this paper proposes a model of combining improved grey wolf optimization (IGWO) and Extreme Learning Machine (ELM) for short-term cloud computing resource load prediction. The improved GWO algorithm is used to search for optimal ELM parameters, which are closely related to the prediction performance of ELM. The experimental results show that the IGWO-ELM model can precisely characterize the complicated trends of cloud computing resource short-term load, and can effectively improve the accuracy and stability of the prediction model compared with the reference models.