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HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network


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1. Amiri, M., L. Mohammad-Khanli. Survey on Prediction Models of Applications for Resources Provisioning in Cloud. – Journal of Network and Computer Applications, Vol. 82, 2017, pp. 93-113.10.1016/j.jnca.2017.01.016Search in Google Scholar

2. Kumar, K. D., E. Umamaheswari. Prediction Methods for Effective Resource Provisioning in Cloud Computing: A Survey. – Multiagent and Grid Systems, Vol. 14, 2018, No 3, pp. 283-305.10.3233/MGS-180292Search in Google Scholar

3. Yang, J., C. Liu, Y. Shang, B. Cheng, Z. Mao, C. Liu, L. Niu, J. Chen. A Cost-Aware Auto-Scaling Approach Using the Workload Prediction in Service Clouds. – Information Systems Frontiers, Vol. 16, 2014, No 1, pp. 7-18.10.1007/s10796-013-9459-0Search in Google Scholar

4. Tang, Z., Y. Mo, K. Li, K. Li, Dynamic Forecast Scheduling Algorithm for Virtual Machine Placement in Cloud Computing Environment. – The Journal of Supercomputing, Vol. 70, 2014, No 3, pp. 1279-1296.10.1007/s11227-014-1227-5Search in Google Scholar

5. Garg, S. K., A. N. Toosi, S. K. Gopalaiyengar, R. Buyya. SLA-Based Virtual Machine Management for Heterogeneous Workloads in a Cloud Datacenter. – Journal of Network and Computer Applications, Vol. 45, 2014, pp. 108-120.10.1016/j.jnca.2014.07.030Search in Google Scholar

6. Chen, Z., Y. Zhu, Y. Di, S. Feng. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network. – Computational Intelligence and Neuroscience, 2015.10.1155/2015/919805432109725691896Search in Google Scholar

7. Urgaonkar, B., P. Shenoy, A. Chandra, P. Goyal, T. Wood. Agile Dynamic Provisioning of Multi-Tier Internet Applications. – ACM Transactions on Autonomous and Adaptive Systems (TAAS), Vol. 3, 2008, No 1, pp. 1-39.10.1145/1342171.1342172Search in Google Scholar

8. Bhargavi, K., B. S. Babu. Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach. – Cybernetics and Information Technologies, Vol. 19, 2019, No 3, pp. 94-117. 9. Calheiros, R. N., R. Ranjan, A. Beloglazov, C. A. De Rose, R. Buyya. CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms. – Software: Practice and Experience, Vol. 41, 2011, No 1, pp. 23-50.Search in Google Scholar

10. Li, T. H. A Hierarchical Framework for Modeling and Forecasting Web Server Workload. – Journal of the American Statistical Association, Vol. 100, 2005, No 471, pp. 748-763.10.1198/016214505000000565Search in Google Scholar

11. Roy, N., A. Dubey, A. Gokhale. Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting. – In: 4th IEEE International Conference on Cloud Computing, 2011, pp. 500-507.10.1109/CLOUD.2011.42Search in Google Scholar

12. Sun, Y. S., Y. F. Chen, M. C. Chen. A Workload Analysis of Live Event Broadcast Service in Cloud. – Procedia Computer Science, Vol. 19, 2013, pp. 1028-1033.10.1016/j.procs.2013.06.143Search in Google Scholar

13. Kumar, K. D., E. Umamaheswari. EWPTNN: An Efficient Workload Prediction Model in Cloud Computing Using Two-Stage Neural Networks. – Procedia Computer Science, Vol. 165, 2019, pp. 151-157.10.1016/j.procs.2020.01.097Search in Google Scholar

14. Khan, A., X. Yan, S. Tao, N. Anerousis. Workload Characterization and Prediction in the Cloud: A Multiple Time Series Approach. – In: IEEE Network Operations and Management Symposium, 2012, pp. 1287-1294.Search in Google Scholar

15. Cao, L. Support Vector Machines Experts for Time Series Forecasting. – Neurocomputing, Vol. 51, 2003, pp. 321-339.10.1016/S0925-2312(02)00577-5Search in Google Scholar

16. Ban, T., R. Zhang, S. Pang, A. Sarrafzadeh, D. Inoue. Referential kNN Regression for Financial Time Series Forecasting. – In: International Conference on Neural Information Processing, 2013, pp. 601-608.10.1007/978-3-642-42054-2_75Search in Google Scholar

17. Messias, V. R., J. C. Estrella, R. Ehlers, M. J. Santana, R. C. Santana, S. Reiff-Marganiec. Combining Time Series Prediction Models Using Genetic Algorithm to Autoscaling Web Applications Hosted in the Cloud Infrastructure. – Neural Computing and Applications, Vol. 27, 2016, No 8, pp. 2383-2406.10.1007/s00521-015-2133-3Search in Google Scholar

18. Kumar, K. D., E. Umamaheswari. Efficient Cloud Resource Scaling Based on Prediction Approaches. – International Journal of Engineering & Technology (UAE), Vol. 7, 2018, No 4.10, pp. 413-416.10.14419/ijet.v7i4.10.21029Search in Google Scholar

19. Islam, S., J. Keung, K. Lee, A. Liu. Empirical Prediction Models for Adaptive Resource Provisioning in the Cloud. – Future Generation Computer Systems, Vol. 28, 2012, No 1, pp. 155-162.10.1016/j.future.2011.05.027Search in Google Scholar

20. Naik, K. B., G. M. Gandhi, S. H. Patil. Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre. – Cybernetics and Information Technologies, Vol. 18, 2018, No 3, pp. 23-36.10.2478/cait-2018-0036Search in Google Scholar

21. Prevost, J. J., K. Nagothu, B. Kelley, M. Jamshidi. Prediction of Cloud Data Center Networks Loads Using Stochastic and Neural Models. – In: 6th International Conference on System of Systems Engineering, 2011, pp. 276-281.10.1109/SYSOSE.2011.5966610Search in Google Scholar

22. Govardhan, P., P. Srinivasan. Enhanced Evolutionary Computing Assisted Robust SLA-Centric Load Balancing System for Mega Cloud Data Centers. – Cybernetics and Information Technologies, Vol. 19, 2019, No 3, pp. 74-93.10.2478/cait-2019-0027Search in Google Scholar

23. Chang, Y. C., R. S. Chang, F. W. Chuang. A Predictive Method for Workload Forecasting in the Cloud Environment. – In: Advanced Technologies, Embedded and Multimedia for Human-Centric Computing, 2014, pp. 577-585.10.1007/978-94-007-7262-5_65Search in Google Scholar

24. Lu, Y., J. Panneerselvam, L. Liu, Y. Wu. RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing. – In: Scientific Programming, toward Smart World. Vol. 2016. 2016.10.1155/2016/5635673Search in Google Scholar

25. Ghobaei-Arani, M., S. Jabbehdari, M. A. Pourmina. An Autonomic Resource Provisioning Approach for Service-Based Cloud Applications: A Hybrid Approach. – Future Generation Computer Systems, Vol. 78, 2018, pp. 191-210.10.1016/j.future.2017.02.022Search in Google Scholar

26. Tizhoosh, H. R. Opposition-Based Learning: A New Scheme for Machine Intelligence. – In: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Vol. 1, 2005, pp. 695-701.Search in Google Scholar

27. Rahnamayan, S., H. R. Tizhoosh, M. M. Salama. Opposition-Based Differential Evolution Algorithms. – In: IEEE International Conference on Evolutionary Computation, 2006, pp. 2010-2017.10.1109/CEC.2007.4424748Search in Google Scholar

28. Hinton, G. E., N. Srivastava, A. Krizhevsky, I. Sutskever, R. R. Salakhutdinov. Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors. – arXiv preprint arXiv:1207.0580, 2012.Search in Google Scholar

29. Bluche, T., C. Kermorvant, J. Louradour. Where to Apply Dropout in Recurrent Neural Networks for Handwriting Recognition? – In: 13th International Conference on Document Analysis and Recognition, 2015, pp. 681-685.10.1109/ICDAR.2015.7333848Search in Google Scholar

30. NASA http Two Months of http Logs from the NASA www Server. ftp://ita.ee.lbl.gov/html/contrib/NASA-HTTP.htmlSearch in Google Scholar

31. Saskatchewan-http-Seven Months of http Logs from the Saskatchewan www Server. ftp://ita.ee.lbl.gov/html/contrib/Sask-HTTP.htmlSearch in Google Scholar

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Sprache:
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Informatik, Informationstechnik