Model for Enhancing Cloud Computing Resource Allocation Management Using Data Analytics
DOI:
https://doi.org/10.51519/journalisi.v6i1.679Keywords:
Ensemble method, Resource allocation, Traffic predictionAbstract
The cloud computing environment requires an adequate and accurate traffic prediction tool to fulfill the needs of customers and support organizations effectively. In the absence of an effective tool for forecasting cloud computing traffic, many organizations might fail. It is difficult to predict the network resources that are suitable to meet the needs of all network clients at a given time in a cloud computing environment because of the inconsistent network traffic flow. There is still room for improving the predictive accuracy of the model in cloud computing. The higher the accuracy of the traffic flow, the better the allocation of resources. Therefore, this study proposes an ensemble method called SGLA (Stepwise Gaussian Linear Autoregressive) by combining linear regression, support vector machines, Gaussian process regression, and the autoregressive integrated moving average technique. SGLA performed better than all methods with a minimum MAPE of 1.03% of the ensemble approach by using the averaging strategy, SGLA shows a clear advantage in handling resource allocation better despite traffic fluctuations, with 91.7% traffic prediction accuracy. Overall experimental results indicate that this method performed better than single models in terms of prediction accuracy. The main contribution of this study is to propose a data analytics model for enhancing cloud computing resource management.
Downloads
References
. N. K. Biswas, S. Banerjee, U. Biswas, and U. Ghosh, “An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing,” Journal of Sustainable Energy Technologies and Assessments, vol. 45, 2021, doi: 10.1016/j.seta.2021.101087
. M. Dongliang, L. Yi, Z. Tao, H. Yanping. “Research on prediction and analysis of supercritical water heat transfer coefficient based on support vector machine,” Journal of Nuclear Engineering and Technology, Vol. 55, no. 11, pp. 4102-4111, 2023, doi: 10.1016/j.net.2023.07.030
. D. Toratani, T. Yoshihara and A. Senoguchi, “Support algorithm for air traffic controllers’ arrival spacing: Improvement of trajectory estimation using Gaussian Process Regression,” Journal of Control Engineering Practice, vol.128, 2022, doi: 10.1016/j.conengprac.2022.105343.
. W. Zhong and L. Du, “Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads,” Journal of Machine Learning and Big Data Analytics for Sustainability and Resilience, vol, 15, 2023, doi: 10.3390/su15042944
. S. Lu, Q. Zhang, G. Chen, and D. Seng: “A combined method for short-term traffic flow prediction based on recurrent neural network", Alexandria Engineering Journal, vol. 60, pp. 87–94, 2021. doi: 10.1016/j.aej.2020.06.008
. G. Zheng, W. K. Chai, V. Katos and M. Walton. “A joint temporal-spatial ensemble model for short-term traffic prediction,” Journal of Neurocomputing, vol. 457, pp.26–39, 2021. doi: 10.1016/j.neucom.2021.06.028
. H. Yan, L. Fu, Q. Yong, and Y. Dong-Jun, “Robust ensemble method for short-term traffic flow prediction,” Journal of Future Generation Computer Systems, vol. 133, pp. 395–410, 2022. doi: 10.1016/j.future.2022.03.034
. X. Chen et al., "Traffic flow prediction by an ensemble framework with data denoising and deep learning model,” journal of Physica, vol. 565, 2021. doi: 10.1016/j.physa.2020.125574
. L. Wenqi et al., “Traffic speed forecasting for urban roads: A deep ensemble neural network model,” Journal of Physica, vol. 593, 2022. doi: 10.1016/j.physa.2022.126988
. A. Knapińska et al., “Long-term prediction of multiple types of time-varying network traffic using chunk-based ensemble learning,” Journal of Applied Soft Computing, vol.130, 2022. doi: 10.1016/j.asoc.2022.109694
. V. Nourania, H. Gökçekuşb and I. K. Umarb, “Artificial intelligence-based ensemble model for prediction of vehicular traffic noise,” journal of Environmental Research, vol.180, 2020. doi: 10.1016/j.envres.2019.108852
. J. Kamiri, and G. Mariga, “Research Methods in Machine Learning: A Content Analysis,"International Journal of Computer and Information Technology, vol. 10, no. 2, 2021.
. Zhou et al., “Genetic Algorithm with Heuristic-based Local Search for multi-dimensional resources scheduling of cloud computing,” journal of Applied Soft Computing, vol.136, 2023. doi: 10.1016/j.asoc.2023.110027
. F. Li, W. Ma, H. Li and J. Li, “Improving Intrusion Detection System Using Ensemble Methods and Over-Sampling Technique,” 2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST), Guangzhou, China, pp. 1200-1205, 2022, doi: 10.1109/IAECST57965.2022.10062178.
. T. Ahmad and N. Zhou, "Ensemble Methods for Probabilistic Solar Power Forecasting: A Comparative Study," 2023 IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5, 2023. doi: 10.1109/PESGM52003.2023.10253133.
. Feng et al, “An ensemble machine learning approach for classification tasks using feature generation,” Journaal of Connection Science, Vol. 35, 2023 doi: 10.1080/09540091.2023.2231168.
. A. B. Ismail, H. B. A. Bakar, and S. B. Shafei, “Comparison of LDPE/corn stalk with eco degradant and LDPE/corn stalk with MAPE: Influence of coupling agent and compatibiliser on mechanical properties,” Materials Today: Proceedings, vol. 31. pp. 360-365, 2020.
. A. V. Agranovskii and A. P. Silukov, “Comparative Analysis of Results of Modern Classification Algorithms Usage for Determining the Type of Physical Activity Based on Integrated Sensors Data,” Wave Electronics and its Application in Information and Telecommunication Systems (WECONF), pp. 1-5, 2021 doi: 10.1109/WECONF51603.2021.9470661.
. Zhang et al., “Security computing resource allocation based on deep reinforcement learning in serverless multi-cloud edge computing,” journal of Future Generation Computer Systems, vol. 151, pp. 152-161, 2024. doi: 10.1016/j.future.2023.09.016.
Downloads
Published
Issue
Section
License
Authors Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
