A Hybrid Technique for Server Consolidation in Cloud Computing Environment

1. Chamas, N., F. Lopez-Pires, B. Baran. Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty. – IEEE Conference, 2017.10.1109/CLEI.2017.8226393Search in Google Scholar

2. Uddin, M., A. A. Rehman. Server Consolidation: An Approach to Make Data Centers Energy Efficient and Green. – International Journal of Scientific and Engineering Research, Vol. 1, 2010, Issue 1.10.14299/ijser.2010.01.002Search in Google Scholar

3. Ali, H. M., D. C. Lee. A Biogeography-Based Optimization Algorithm for Energy Efficient Virtual Machine Placement. – IEEE Symposium on Swarm Intelligence, 2014.10.1109/SIS.2014.7011800Search in Google Scholar

4. Panigrahy, R., K. Talwar, L. Uyeda, U. Wieder. Heuristics for Vector Bin Packing, 2011.Search in Google Scholar

5. Agrawal, S., S. Bose, S. Sundarrajan. Grouping Genetic Algorithm for Solving the Server Consolidation Problem with Conflicts. Genetics and Evolutionary Computation. – In: Proc. of 1st ACM/SIGEVO Summit, ACM, 2009.10.1145/1543834.1543836Search in Google Scholar

6. Ali, R., Y. Shen, X. Huang, J. Zhang, A. Ali. VMR: Virtual Machine Replacement Algorithm for QoS and Energy-Awareness in Cloud Data Centers. – In: IEEE International Conference on Computational Science and Engineering, 2017.Search in Google Scholar

7. Liu, X., Z. Zhan, J. D. Deng, Y. Li, T. Gu, J. Zhang. An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing. – IEEE Transactions on Evolutionary Algortihm, Vol. 22, 2018, Issue 1.10.1109/TEVC.2016.2623803Search in Google Scholar

8. Dorterler, S., M. Dorterler, S. Ozdemir. Multi-Objective Virtual Machine Placement Optimization for Cloud Computing. – In: IEEE International Symposium on Networks, Computers and Communication, 2017.10.1109/ISNCC.2017.8072013Search in Google Scholar

9. Sotomayor, B. Provisioning Computational Resources Using Virtual Machines and Leases. PhD Thesis Submitted to the University of Chicago, USA, 2010.Search in Google Scholar

10. Kansal, N. J., I. Chana. Energy-Aware Virtual Machine Migration for Cloud Computing – A Firefly Optimization Approach. – Journal of Grid Computing, SpringerLink, Vol. 14, 2016, Issue 2, pp. 327-345.10.1007/s10723-016-9364-0Search in Google Scholar

11. Zhou, A., S. Wang, B. Cheng, Z. Zheng, F. Yang, R. N. Chang, M. R. Lyu, Rajkumar Buyya. Cloud Service Reliability Enhancement via Virtual Machine Placement Optimization. – IEEE Transactions on Services Computing, Vol. 10, 2017, No 6.10.1109/TSC.2016.2519898Search in Google Scholar

12. Fu, X., C. Zhou. Predicted Affinity Based Virtual Machine Placement in Cloud Computing Environments. – IEEE Transaction on Cloud Computing, Vol. 13, 2014, No 9.Search in Google Scholar

13. Khosravi, A., L. L. H. Andrew, Rajkumar Buyya. Dynamic VM Placement Method for Minimizing Energy and Carbon Cost in Geographically Distributed Cloud Data Centers. – IEEE Transactions on Sustainable Computing, Vol. 2, 2017, No 2.10.1109/TSUSC.2017.2709980Search in Google Scholar

14. Gao, Y., H. Guan, Z. Qi, Y. Hou, L. Liu. A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement in Cloud Computing. – Journal of Computer and System Sciences, Vol. 79, 2013, No 8, pp. 1230-1242.10.1016/j.jcss.2013.02.004Search in Google Scholar

15. Kumar, D., Dr. Tarni Mandal. Bi-Objective Virtual Machine Placement Using Hybrid of Genetic Algorithm and Particle Swarm Optimization in Cloud Data Center. – International Journal of Applied Engineering Research, Vol. 12, 2017, No 22, pp. 12044-12051. ISSN 0973-4562.Search in Google Scholar

16. Zhang, J., X. Wang, H. Huang, S. Chen. Clustering Based Virtual Machines Placement in Distributed Cloud Computing. – Future Generation Computer Systems, Elsevier, Vol. 66, 2017, pp.1-10.10.1016/j.future.2016.06.018Search in Google Scholar

17. Barlaskar, E., N. Ajith Singh, Yumnum, Y. J. Singh. Energy Optimization Methods for Virtual Machine Placement in Cloud Data Center, ADBU. – Journal of Engineering and Technology, 2014.Search in Google Scholar

18. Tawfeek, M. A., A. B. El-Sisi, A. E. Keshk, F. A. Torkey. Virtual Machine Placement Based on Ant Colony Optimization for Minimizing Resource Wastage. – In: International Conference on Advanced Machine Learning Technologies and Applications (AMLTA’14), 2014, p.153.10.1007/978-3-319-13461-1_16Search in Google Scholar

19. Alboaneen, D. A., H. Tianfield, Y. Z. Glasgow. Glowworm Swarm Optimization Algorithm for Virtual Machine Placement in Cloud Computing. – In: Proc. of IEEE International Conference on Cloud and Big Data Computing, 2016, pp. 808-814.10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0129Search in Google Scholar

20. Speitkamp, B., M. Bichler. A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers. – IEEE Transactions on Services Computing, 2010, pp. 266-278.10.1109/TSC.2010.25Search in Google Scholar

21. Sait, S. M., A. Bala, A. H. El-Maleh. Cuckoo Search Based Resource Optimization of Datacenters. – Applied Intelligence, 2015, pp. 1-18.10.1007/s10489-015-0710-xSearch in Google Scholar

22. Riquelme, N., C. V. Lucken, B. Baran. Performance Metrics in Multi-Objective Optimization. – In: IEEE Latin American Computing Conference, 2015.10.1109/CLEI.2015.7360024Search in Google Scholar

23. Wu, G., M. Tang, Y. C. Tian, W. Li. Energy Efficient Virtual Machine Placement in Data Centers by Genetic Algorithm. – In: International Conference on Neural Information Processing (ICONIP), Neural Information Processing, 2012, pp. 315-323.10.1007/978-3-642-34487-9_39Search in Google Scholar

24. Zhao, L., L. Lu, Z. Jin, C. Yu. Online Virtual Machine Placement for Increasing Cloud Provider’s Revenue. – IEEE Transactions on Services Computing, Vol. 10, 2017, No 2.10.1109/TSC.2015.2447550Search in Google Scholar

25. Shabeera, T. P., S. D. Madhukumar, S. M. Salam, K. Muralikrishnan. Optimizing VM Allocation and Data Placement for Data-Intensive Applications in Cloud Using ACO Metaheuristic Algorithm. – International Journal of Engineering Science and Technology, Vol. 20, 2017, Issue 2, pp. 616-628.10.1016/j.jestch.2016.11.006Search in Google Scholar

26. Gagwero, M. G., L. Caviglione. Model Predictive Control for Energy Efficient, Quality-Aware Virtual Machine Placement. – IEEE Transactions on Automation Science and Engineering, 2018, Issue 1, pp. 1-13.10.1109/TASE.2018.2826723Search in Google Scholar

27. Jing, H. Z., W. F. Liu, Q. Wang, W. Zhang, Q. Zheng. Power-Aware and Performance – Guaranteed Virtual Machine Placement in the Cloud. – IEEE Transactions on Parallel and Distributed Systems, Vol. 29, 2018, No 6.10.1109/TPDS.2018.2794369Search in Google Scholar

28. Hung, N. Q., P. D. Nien, N. H. Nam, N. H. Tuong, N. Thoai. A Genetic Algorithm for Power-Aware VirtuaMachine Allocation in Private Cloud. – Lecture Notes in Computer Science, Vol. 7804, 2013.Search in Google Scholar

29. Sarvesh, Kumar. Discrete Gravitational Search Algorithm for Virtual Machine Placement in Cloud Computing. – International Journal of Pure and Applied Mathematics, Vol. 117, 2017, No 19, pp. 337-342.Search in Google Scholar

30. Boominathan, P., M. Aramudan, Ra. K. Saravanaguru. Fuzzy Bio-Inspired Hybrid Techniques for Server Consolidation and Virtual Machine Placement in Cloud Environment. – Cybernetics and Information Technologies, Vol. 17, 2017, No 4, pp. 52-68.10.1515/cait-2017-0041Search in Google Scholar

31. Mirjalili, S., S. M. Mirjalili, A. Lewis. Grey Wolf Optimizer. – Journal in Advances in Engineering Software, Vol. 69, 2014, pp. 46-61, ScienceDirect.10.1016/j.advengsoft.2013.12.007Search in Google Scholar

32. Joshi, S., J. C. Bansal. Grey Wolf Gravitational Search Algorithm. – International Workshop on Computational Intelligence (IWCI), IEEE, 2016.10.1109/IWCI.2016.7860371Search in Google Scholar

33. Maryuma, K., S. K. Chang, D. T. Tang. A General Packing Algorithm for Mutidimensional Resource Requirements. – International Journal of Computer and Information Sciences, Vol. 6, 1977, Issue 2, pp. 131-149.10.1007/BF00999302Search in Google Scholar

34. Rodriguez, L., O. Castillo, J. Soria. Grey Wolf Optimizer with Dynamic Adaptation Parameters Using Fuzzy Logic. – In: IEEE Congress on Evolutionary Computation(CEC), 2016.10.1109/CEC.2016.7744183Search in Google Scholar

35. Mirjalili, S., S. Saremi, S. M. Mirjalili, L. S. Coelho. Multiobjective Grey Wolf Optimizer: A Novel Algorithm for Multi-Criterion Optimization. – Elsevier Journal of Expert Systems with Applications, 2015.10.1016/j.eswa.2015.10.039Search in Google Scholar