References
- 1. Fraser, C. K., et al. Live Migration of Virtual Machines. - In: Proc. of 2nd USENIX Symposium on Networked Systems Design and Implementation, Berkeley, CA, 2005, pp. 273-286.
- 2. Vogels, W. Beyond Server Consolidation. - ACM Queue, 2008, No 1, pp. 20-26.
- 3. Feller, E., C. Morin et al. A Case for Fully Decentralized Dynamic VM Consolidation in Clouds. - In: Proc. of 4th IEEE International Conference, Cloud Computing Technology and Science, Taipei, Taiwan, 2012, pp. 26-33.
- 4. Murtazaev, S. O. Sercon: Server Consolidation Algorithm Using Live Migration of Virtual Machines for Green Computing. - IETE Technical Review, Vol. 28, 2011, No 3, pp. 212-231.
- 5. Marzolla, M., O. Babaoglu et al. Server Consolidation in Clouds through Gossiping. -In: Proc. of 12th IEEE International Symposium, World of Wireless, Mobile and Multimedia Networks, Lucca, Italy, 2011, pp. 1-6.
- 6. Beloglazov, J. A., et al. Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing. - Grid Computing and e-Science, Future Generation Computer Systems, Vol. 28, 2012, pp. 755-768.
- 7. Beloglazov, R. B. Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers. - Concurrency and Computation: Practice and Experience, Vol. 24, 2012, No 13, pp. 1397-1420.
- 8. Farahnakian, F., P. Liljeberg et al. Linear regression Based CPU Usage Prediction Algorithm for Live Migration of Virtual Machines in Data Centers. - In: Proc. of 39th Euromicro Conference of Software Engineering and Advanced Applications, Santander, Spain, 2013, pp. 357-364.
- 9. Farahnakian, F., T. Pahikkala et al. Energy Aware Consolidation Algorithm Based on K-Nearest Neighbor Regression for Cloud Data Centers. - In: Proc. of 6th IEEE/ACM International Conference on Utility and Cloud Computing, Dresden, Germany, 2013.
- 10. Wood, T., P. Shenoy et al. Sandpiper: Black-Box and Gray-Box Resource Management for Virtual Machines. - Computer Networks, Vol. 53, 2009, pp. 2923-2938.
- 11. Ajiro, Y., A. Tanaka. Improving Packing Algorithms for Server Consolidation. - In: Proc. of International Conference for the Computer Measurement Group, San Diego, California, USA, 2007, pp. 399-407.
- 12. Wang, M., X. Meng et al. Consolidating Virtual Machines with Dynamic Bandwidth Demand in Data Centers. - In: Proc. of 30th IEEE International Conference on Computer Communications, Shanghai, China, 2011, pp. 71-75.
- 13. Harman, M., K. Lakhotia et al. Cloud Engineering is Search Based Software Engineering Too. - Journal of Systems and Software, Vol. 86, 2013, No 9, pp. 2225-2241.
- 14. Dorigo, M., G. Di Caro et al. Ant Algorithms for Discrete Optimization. - Artificial Life, Vol. 5, 1999, No 2, pp. 137-172.
- 15. Dorigo, M., L. Gambardell a. Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. - IEEE Transactions on Evolutionary Computation, Vol. 1, 1997, No 1, pp. 53-66.
- 16. Barbagallo, D., E. Di Nitto et al. A Bio-Inspired Algorithm for Energy Optimization ina Self-Organizing Data Center. - Self-Organizing Architectures, Springer, 2010, pp. 127-151.
- 17. Chen , H., L. Xiong et al. Cloud Task Scheduling Simulation via Improved Ant Colony Optimization Algorithm. - Journal of Convergence Information Technology, 2013.
- 18. Dong, Y. S., G. C. Xu et al. A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform. - The Scientific World Journal, 2014, pp. 1-12.
- 19. Feller, E. E. et al. Energy-Aware Distributed Ant Colony Based Virtual Machine Consolidation in Iaa S Clouds Bibliographic Study. - Informatics Mathematics (INRIA), 2012, pp. 1-13.
- 20. Ferdaus, M. H., M. Murshed et al. Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic. - In: Proc. of 20th International Conference Euro-Par 2014 Parallel Processing, Porto, Portugal, 2014, pp. 306-317.
- 21. Zhong, H., K. Tao et al. An Approach to Optimized Resource Scheduling Algorithm for Open-Source Cloud Systems. - In: Proc. of China Grid Conference (China Grid), 2010, Fifth Annual, Guangzhou, China, 2010, pp. 124-129.
- 22. Madhusudha n, B., K. C. Sekaran. A Genetic Algorithm Approach for Virtual Machine Placement in Cloud. - In: Proc. of International Conference on Emerging Research in Computing, Information, Communication and Applications, 2013, pp. 115-122.
- 23. Tang, M., S. Pan. A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers. - Neural Processing Letters, Vol. 41, 2015, No 2, pp. 211-221.
- 24. Cleveland, W. S. Robust Locally Weighted Regression and Smoothing Scatterplots. - Journal of the American Statistical Association, Vol. 74, 1979, No 368, pp. 829-836.
- 25. Verma, G. D. et al. Server Workload Analysis for Power Minimization Using Consolidation. - In: Proc. of 2009 USENIX Annual Technical Conference, San Diego, China, 2009, pp. 28-42.
- 26. Abdi, H. Multiple Correlation Coefficient. - N. J. Salkind, Ed. Sage, Thousand Oaks, CA, USA, 2007.
- 27. Park, K. S., V. S. Pai. Co Mon: A Mostly-Scalable Monitoring System for Planet-Lab. - ACM SIGOPS Operating Systems Review, Vol. 40, 2006, No 1, pp. 65-74.
- 28. Theja, P. R., S. K. K. Babu. An Evolutionary Computing Based Energy Efficient VM Consolidation Scheme for Optimal Resource Utilization and Qo S Assurance. - Indian Journal of Science and Technology, 77179, Vol. 8, 2015, No 26, pp. 1-11.
- 29. Theja, P. R., S. K. K. Babu. An Adaptive Genetic Algorithm Based Robust Qo S Oriented Green Computing Scheme for VM Consolidation in Large Scale Cloud Infrastructures. - Indian Journal of Science and Technology, 79175, Vol. 8, 2015, No 27, pp. 1-13.