ABSTRACT
Cloud computing datacenters provide thousands to millions of virtual machines (VMs) on-demand in highly dynamic environments, requiring quick placement of requested VMs into available physical machines (PMs). Due to the randomness of customer requests, the Virtual Machine Placement (VMP) should be formulated as an online optimization problem. This work presents a formulation of a VMP problem considering the optimization of the following objective functions: (1) power consumption, (2) economical revenue, (3) quality of service and (4) resource utilization. To analyze alternatives to solve the formulated problem, an experimental comparison of five different online deterministic heuristics against an offline memetic algorithm with migration of VMs was performed, considering several experimental workloads. Simulations indicate that First-Fit Decreasing algorithm (A4) outperforms other evaluated heuristics on average. Experimental results prove that an offline memetic algorithm improves the quality of the solutions with migrations of VMs at the expense of placement reconfigurations.
- A. Anand, J. Lakshmi, and S. Nandy. Virtual machine placement optimization supporting performance SLAs. In Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on, volume 1, pages 298--305. IEEE, 2013. Google ScholarDigital Library
- L. A. Barroso and U. Hölzle. The case for energy-proportional computing. IEEE computer, 40(12):33--37, 2007. Google ScholarDigital Library
- A. Beloglazov, J. Abawajy, and R. Buyya. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5):755--768, 2012. Google ScholarDigital Library
- A. Beloglazov and R. Buyya. 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, 24(13):1397--1420, 2012. Google ScholarDigital Library
- D. Dong and J. Herbert. Energy efficient VM placement supported by data analytic service. In Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on, pages 648--655. IEEE, 2013.Google ScholarDigital Library
- S. Fang, R. Kanagavelu, B.-S. Lee, C. H. Foh, and K. M. M. Aung. Power-efficient virtual machine placement and migration in data centers. In Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing, pages 1408--1413. IEEE, 2013. Google ScholarDigital Library
- T. Ferreto, C. A. De Rose, and H.-U. Heiss. Maximum migration time guarantees in dynamic server consolidation for virtualized data centers. In Euro-Par Parallel Processing, pages 443--454. Springer, 2011. Google ScholarDigital Library
- M. Gahlawat and P. Sharma. Survey of virtual machine placement in federated clouds. In Advance Computing Conference (IACC), 2014 IEEE International, pages 735--738, Feb 2014.Google ScholarCross Ref
- D. Ihara, F. López-Pires, and B. Barán. Many-objective virtual machine placement for dynamic environments. In Proceedings of the 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing. IEEE Computer Society, 2015.Google Scholar
- H. Jin, D. Pan, J. Xu, and N. Pissinou. Efficient VM placement with multiple deterministic and stochastic resources in data centers. In Global Communications Conference (GLOBECOM), 2012 IEEE, pages 2505--2510. IEEE, 2012.Google ScholarCross Ref
- F. López-Pires and B. Barán. Multi-objective virtual machine placement with service level agreement: A memetic algorithm approach. In Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, pages 203--210. IEEE Computer Society, 2013. Google ScholarDigital Library
- F. López-Pires and B. Barán. A many-objective optimization framework for virtualized datacenters. In Proceedings of the 2015 5th International Conference on Cloud Computing and Service Science, 2015.Google ScholarCross Ref
- F. López-Pires and B. Barán. A virtual machine placement taxonomy. In Proceedings of the 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud and Grid Computing. IEEE Computer Society, 2015.Google Scholar
- P. Mell and T. Grance. The NIST definition of cloud computing. National Institute of Standards and Technology, 53(6):50, 2009.Google Scholar
- J. Ortigoza, F. López Pires, and B. Barán. A taxonomy on dynamic environments for provider-oriented virtual machine placement. In Proceedings of the 2016 IEEE 4th International Conference on Cloud Engineering. IEEE Computer Society, 2016.Google ScholarCross Ref
- S. B. Salem, M. Fakhfakh, D. S. Masmoudi, M. Loulou, P. Loumeau, and N. Masmoudi. A high performances cmos ccii and high frequency applications. Analog Integrated Circuits and Signal Processing, 49(1):71--78, 2006. Google ScholarDigital Library
- B. Speitkamp and M. Bichler. A mathematical programming approach for server consolidation problems in virtualized data centers. Services Computing, IEEE Transactions on, 3(4):266--278, 2010. Google ScholarDigital Library
- Q. Zheng, R. Li, X. Li, N. Shah, J. Zhang, F. Tian, K.-M. Chao, and J. Li. Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Future Generation Computer Systems, 2015. Google ScholarDigital Library
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