Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network
Dublin Core
Title
Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network
Subject
Mobile cloud
LSTM Network
Glowworm Swarm Optimization Algorithm
Load Forecasting.
Description
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.
Creator
Sudhakaran, P.
Swaminathan, Subbiah
Yuvaraj, D.
Priya, S.Shanmuga
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 05 (2020); pp. 150-163
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2020-04-07
Rights
Copyright (c) 2020 P. Sudhakaran, Subbiah Swaminathan, D. Yuvaraj, S.Shanmuga Priya
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
P Sudhakaran. et al., Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network, International Association of Online Engineering (IAOE), Vienna, Austria, 2020, accessed November 7, 2024, https://igi.indrastra.com/items/show/1667