Revolutionizing Cloud Resource Allocation: Harnessing Layer-Optimized Long Short-Term Memory for Energy-Efficient Predictive Resource Management
DOI:
https://doi.org/10.4108/ew.6505Keywords:
Long-short-term memory, cloud computing, energy-effecient resources, resources forecastingAbstract
INTRODUCTION: This is the introductory text. Accurate data center resource projection will be challenging due to the dynamic and constantly changing workloads of multi-tenant co-hosted applications. Resource Management in the Cloud (RMC) becomes a significant research component. In the cloud's easy service option, users can choose to pay a fixed sum or based on the amount of time.
OBJECTIVES: The main goal of this study is systematic method for estimating future cloud resource requirements based on historical consumption. Resource distribution to users, who require a variety of resources, is one of cloud computing main objective in this study.
METHODS: This article suggests a Layer optimized based Long Short-Term Memory (LOLSTM) to estimate the resource requirements for upcoming time slots. This model also detects SLA violations when the QoS value exceeds the dynamic threshold value, and it then proposes the proper countermeasures based on the risk involved with the violation.
RESULTS: Results indicate that in terms of training and validation the accuracy is 97.6%, 95.9% respectively, RMSE and MAD shows error rate 0.127 and 0.107, The proposed method has a minimal training and validation loss at epoch 100 are 0.6092 and 0.5828, respectively. So, the suggested technique performed better than the current techniques.
CONCLUSION: In this work, the resource requirements for future time slots are predicted using LOLSTM technique. It regularizes the weights of the network and avoids overfitting. In addition, the proposed work also takes necessary actions if the SLA violation is recognized by the model. Overall, the proposed work in this study shows better performance compared to the existing methods.
Downloads
References
Tseng F-H, Wang X, Chou L-D, Chao H-C, Leung VCM. Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm. IEEE Syst J. 2018; 12(2):1688–1699. DOI: https://doi.org/10.1109/JSYST.2017.2722476
Nguyen HM, Kalra G, Kim D. Host load prediction in cloud computing using Long Short-Term Memory Encoder–Decoder. J Supercomput. 2019; 75(11):7592–7605. DOI: https://doi.org/10.1007/s11227-019-02967-7
Song T, Wang Y, Li G, Pang S. Server consolidation energy-saving algorithm based on resource reservation and resource allocation strategy. IEEE Access. 2019; 7:171452–171460. DOI: https://doi.org/10.1109/ACCESS.2019.2954903
Karthiban K, Raj JS. An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm. Soft Comput. 2020; 24(19):14933–14942. DOI: https://doi.org/10.1007/s00500-020-04846-3
Baig S-U-R, Iqbal W, Berral JL, Erradi A, Carrera D. Adaptive prediction models for data center resources utilization estimation. IEEE Trans Netw Serv Manag. 2019; 16(4):1681–1693. DOI: https://doi.org/10.1109/TNSM.2019.2932840
Chien W-C, Lai C-F, Chao H-C. Dynamic resource prediction and allocation in C-RAN with edge artificial intelligence. IEEE Trans Industr Inform. 2019; 15(7):4306–4314. DOI: https://doi.org/10.1109/TII.2019.2913169
Yu P, Zhou F, Zhang X, Qiu X, Kadoch M, Cheriet M. Deep learning-based resource allocation for 5G broadband TV service. IEEE Trans On Broadcast. 2020; 66(4):800–813. DOI: https://doi.org/10.1109/TBC.2020.2968730
Zhang Q, Yang LT, Yan Z, Chen Z, Li P. An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans Industr Inform. 2018; 14(7):3170–3178. DOI: https://doi.org/10.1109/TII.2018.2808910
Haytamy S, Omara F. A deep learning based framework for optimizing cloud consumer QoS-based service composition. Computing. 2020; 102(5):1117–1137. DOI: https://doi.org/10.1007/s00607-019-00784-7
Praveenchandar J, Tamilarasi A. RETRACTED ARTICLE: Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing. J Ambient Intell Humaniz Comput. 2021; 12(3):4147–4159. DOI: https://doi.org/10.1007/s12652-020-01794-6
Hussain W, Hussain FK, Hussain O, Bagia R, Chang E. Risk-based framework for SLA violation abatement from the cloud service provider’s perspective. Comput J. 2018; 61(9):1306–1322. DOI: https://doi.org/10.1093/comjnl/bxx118
Banerjee S, Roy S, Khatua S. Efficient resource utilization using multi-step-ahead workload prediction technique in cloud. J Supercomput. 2021; 77(9):10636–10663. DOI: https://doi.org/10.1007/s11227-021-03701-y
Rendyk. 2021 Tuning the hyperparameters and layers of neural network deep Learning. Analytics Vidhya. [accessed 2023 Aug 25]. https://www.analyticsvidhya.com/blog/2021/05/tuning-the-hyperparameters-and-layers-of-neural-network-deep-learning.
Xiao H, Sotelo MA, Ma Y, Cao B, Zhou Y, Xu Y, Wang R, Li Z. An improved LSTM model for behavior recognition of intelligent vehicles. IEEE Access. 2020; 8:101514–101527. doi:10.1109/access.2020.2996203. http://dx.doi.org/10.1109/access.2020.2996203. DOI: https://doi.org/10.1109/ACCESS.2020.2996203
Gul B, Khan IA, Mustafa S, Khalid O, Khan A ur R. CPU–RAM-based energy-efficient resource allocation in clouds. J Supercomput. 2019; 75(11):7606–7624. DOI: https://doi.org/10.1007/s11227-019-02969-5
Anoop S, Singh JAP. RETRACTED ARTICLE: Multi-user energy efficient secured framework with dynamic resource allocation policy for mobile edge network computing. J Ambient Intell Humaniz Comput. 2021; 12(7):7317–7332. DOI: https://doi.org/10.1007/s12652-020-02407-y
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Energy Web
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
