Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network

P. Sudhakaran, Subbiah Swaminathan, D. Yuvaraj, S.Shanmuga Priya


Focusing on the issue of host load estimating in mobile cloud computing, the Long Short Term Memory networks (LSTM)is introduced, which is appropriate for the intricate and long-term arrangement information of the cloud condition and a heap determining calculation dependent on Glowworm Swarm Optimization LSTM neural system 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 in prediction accuracy higher than equivalent prediction algorithms.


Mobile cloud; LSTM Network; Glowworm Swarm Optimization Algorithm; Load Forecasting.

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International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923
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