Load Forecasting model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network
Zhenhua Zhang, Wei Zhu, Wei Zhong, Yi Zhuang · 2019
Aiming at the problem of host load forecasting in mobile cloud computing, the Long Short Term Memory networks (LSTM) is introduced, which is suitable for the complex and long-time series data of the cloud environment and a load forecasting algorithm based on Glowworm Swarm Optimization LSTM neural network is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are superior to similar prediction algorithms in prediction accuracy.