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

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