A Two Phase Optimization Scheme with Efficient Prediction-based Triggering for Virtual Machine Placement in Cloud Datacenters

  • Rahimatu Hayatu Yahaya Bayero University Kano
  • Faruku Umar Ambursa Bayero University Kano
  • Bashir Galadanci Bayero University Kano
Keywords: Virtual Machine Placement, Online and Offline VM Scheduling, Two-phase Optimization.


Cloud services have become one of the most critical IT infrastructures of today offering easy access to multiple services. Virtualization enables multiple clients and enterprises to leverage such cloud services simultaneously in order to optimize the use of resources and reduce the physical systems available. Virtual Machine Placement (VMP) is concerned with selecting appropriate physical machines for each virtual machine request. This work proposed an improved two-phase optimization scheme for the Virtual Machine Placement problem. The considered two-phase optimization scheme incorporated the features and advantages of both online (dynamic) and offline (static) VMP formulations, where a predictive VMPr triggering technique is proposed to decide when to cause a recalculation phase of the placement. Taking into account 400 different scenarios, experimental evaluation was performed against the benchmark research and the results show that the proposed VMPr triggering technique outperforms the benchmark work by achieving a minimum cost function.


Adamuthe, A., Pandharpatte, R., & Thampi, G. (2013). multiobjective virtual machine placement in cloud environment. in Cloud & Utibigquitous Computing & Emerging Technologies (CUBE),2013 International Conference on IEEE, vol. 57, pp. 8-13.
Alahmadi, A., Alnowiser, A., Zhu, M., Che, D., & Ghodous, P. (2014). Enhanced FirstFit Decreasing Algorithm for Energy-Aware job Scheduling in Cloud . In Computational Science and Computational Intelligence (CSCI), 2014 International Conference (pp. 69-74). IEEE.
Alharbi, H. A., El-Gorashi, T. E., & Elmirghani, A. Q. (2017). Energy Efficient Virtual Machine placement in IP over WDM Networks. In: 19th International Conference on Transarent Optical Networks (ICTON 2017). Girona, Spain: IEEE. Retrieved 06 02, 2017, from https//doi.org/10.1109/ICTON.2017.8024957
Aloulou, M. A., & Croce, F. D. (2008). Complexity of single machine scheduling problems under scenario based uncertainty. Oper. Res. Lett, (pp. 338-342).
Aloulou, M. A., & Croce, F. D. (2008). Complexity of single machine scheduling problems under scenario based uncertainty. Oper. Res. Lett, 338-342.
Amarilla, A., Benıtez, L., Zalimben, S., Pires, L. F., & Baran, B. (2017). Evaluating a Two-Phase Virtual Machine Placement Optimization Scheme for Cloud Computing Datacenters. MIC/MAEB 2017, (pp. 4-7). Barcelona.
Anand, A., Lakshmi, J., & Nandy, S. (2013). virtual machine plcement optimization supporting performance sla in cloud computing technology and science (CloudCom). IEEE 5th International Conference (pp. 298-305). IEEE.
Angeles, S. (2014). Virtualization Vs Cloud Computing: What's the Difference? BusinessNewsDaily.
Ashu, Kaur, A., Singh, D. m., & Singh, P. (2017). A Taxonomy and survey on placement of virtual machines in cloud. International conference on Energy, Communications, Data analystics and Soft computing (ICECDS), (pp. 2054-2058).
Azizi, S., Zandsalimi, M., & Li, D. (2020). An energy-efficient algorithm for virtual machine placement optimization in cloud data centers. Cluster Computing. Retrieved from https://doi.org/10.1007/s10586-020-03096-0
Baloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy aware resource allocation heuristics for efficient management of datacenters for cloud Computing. Future Gener. Comp. Syst., 755-768.
Bin, E., Biran, O., Boni, O., Hadad, E., Kolodner, E. K., Moatti, Y., & Lorenz, D. H. (2011). Guaranteeing high availability goals for virtual machine placement in Distributed Computing Systems (ICDCS). 31st International Conference (pp. 700-709). IEEE.
Biran, O., Corradi, A., Fanelli, M., Foschini, L., Nus, A., Raz, D., & Silvera, E. (2012). A stable network aware VM placement for Cloud Systems. in proceedings of the 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012) (pp. 498-506). IEEE Computer Society.
Braiki, K., & Youssef, H. (2019). Fuzzy-logic-based multi-objective best-fit-decreasing virtual machine reallocation. The Journal of Supercomputing. Retrieved from https://doi.org/10.1007/s11227-019-03029-8
Breitgand, D., & Epstein, A. (2011). Sla-aware placement of multi-virtual machine elastic services in compute clouds. IM,IFI/IEEE International Sympodium on IEEE (pp. 161-168). In: Integrated Network management.
Calcavecchia, N. M., Biran, O., Hadad, E., & Moatti, Y. (2012). VM placement strategies for cloud scenarios in cloud computing (CLOUD). IEEE 5th International Conference (pp. 852-859). IEEE.
Chaisiri, S.; Lee, B-S.; Niyato, D. (2009). Optimal Virtual Machine Placement across multiple Cloud Providers. in Services Computing Conference, 2009. APSCC. IEEE Asia Pasific. (pp. 103-110). IEEE.
Chamas, N., Lopez, F. P., & Baran, B. (2017). Two Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty. IEEE.
Chang, Y., Gu, C., & Luo, F. (2016). A novel energy‐aware and resource efficient virtual resource allocation strategy in IaaS cloud. In Computer and Communications (ICCC), 2016 2nd IEEE International Conference (pp. 1283‐1288). IEEE.
Chen, K. Y., Xu, Y., Xi, K., & Chao, H. J. (2013). Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems. in communications (ICC), 2013 IEEE International Conference (pp. 3498-3503). IEEE.
Dong, J., Wang, H., Jin, X., Li, Y., Zhang, P., & Cheng, S. (2013). Virtual machine placement for improving energy efficiency an network performance in IaaS cloud. in Distributed Computing Systems Workshops(ICDSW), 2013 IEEE 33rd International Conference (pp. 238-243). IEEE.
Elmoroth, E., Tordsson, J., Hernandez, F., Ali-Eldin, A., Svard, P., Sedaghat, M., & Li, W. (2011). self-management challenges for multi-clouds architechtures in towards a service-based Internet. Springer, 38-49.
Fang, S., Kanagavelu, R., Lee, B.-S., Foh, C., & Aung, K. (2013). Power-Efficient virtual machine placement and migration in datacenters. IEEE International Conference (pp. 1408-1413). in Green Computing and Communications, GreenCom, 2013 IEEE and Internet of Things, IThings/CSPCom.
Fang, W., Liang, X., Li, S., Chiaraviglio, L., & Xiong, N. (2013). VMPlanner: Optimising virtual machine placement and traffic flow routing to reduce network power cost in cloud datacenters. Computer Networks, vol.57(1), 179-196.
Farahnakian, F., Bahsoon, R., Liljeberg, P., & Pahikkala, T. (2016). Self-adaptive Resource Management System in IaaS clouds. IEEE 9th International Conference on Cloud Computing.
Farahnakin, F., Bahsoon, R., Liljeberg, P., & Pahikkala, T. (2016). Self-Adaptive resource management system in IaaS clouds,. In P. L. R. Bahsoon (Ed.), 9th International Conference on Cloud Computing, IEEE CLOUD, (p. 553560). IEEE.
Feller, E., Morin, C., & Esnault, A. (2012). A case for fully decentralized dynamic VM consolidation in clouds. in Cloud Computing technology and Science, CloudCom,2012 IEEE 4th International Conference (pp. 26-33). IEEE.
Feller, E., Rilling, L., & Morin, C. (2011). Energy-Aware Ant Colony Based Workload Placement. in Clouds. The 12th IEEE/ACM International Conference on Grid Computing (GRID-2011). Lyon, France: IEEE/ACM.
Ferreto, T., Netto, M., Calheiros, R., & Rose, C. D. (2011). server consolidation with migration control for virtualized data centers. Future Generation Computer Systems, vol.27, 1027-1034.
Gao, Y., Guan, H., Qi, Z., Hou, Y., & Liu, L. (2013). A multi objective ant colony system algorithm forr virtual machine placement in cloud computing. Journal of Computer and System Sciences, vol 79, 1230-1242.
Gardner, E. S., & McKenzie, E. (1985). Forecasting trends in time series. Management Science,, 1237–1246.
Goudarzi, H., & Pedram, M. (2012). Energy-efficiency virtual machine replication and placement in a cloud computing system. in the cloud computing (CLOUDS), . IEEE 5th International conference.
Grance, P. M. (2009). The NIST defination of cloud computing. National Institute of Standards and technology.
Gupta, A., Milojicic, D., & Kal'e, L. V. (2012). Optimizing VM placement for hpc in cloud. in proceedings of the 2012 workshop on Cloud Services, federation and 8th open cirrus summit, ACM, (pp. 1-6).
Haghighi, M. A., Maeen, M., & Haghparast, M. (2018). An Energy-Efficient Dynamic Resource Management Approach Based on Clustering and Meta-Heuristic Algorithms in Cloud Computing IaaS Platforms. Wireless Personal Communications. Retrieved from https://doi.org/10.1007/s11277-018-6089-3
Hoeflin, D., & Reeser, P. (2012). Quantifying the performance impact of overbooking virtualized resources. in Communications (ICC), 2012 IEEE International Conference (pp. 5523-5527). IEEE.
Hong, H.-J., Chen, D.-Y., Huang, C.-Y., Chen, K.-T., & Hsu, C.-H. (2013). Qoe-aware virtual machine placement for cloud games . in Network and Systems Support for Games (NetGames), 2013 12th Annual Workshop on IEEE, (pp. 1-2).
Huang, J., Li, C., & J. Yu. (2012). Resource prediction based on double exponential smoothing. in: 2012 2nd International Conference on Consumer Electronics, Communications and Networks, CECNet (pp. 2056-2060). CECNet.
Hyndman, R., & Athanasopoulos, G. (2018). Forecasting: principles and practice. Melbourne, Australia: OTexts. Retrieved from OTexts.com/fpp2
Ihara, D., Pires, F. L., & Baran, B. (2015). Many-objectivevirtualmachine placement for Dynamic Environments.
Jayasinghe, D., Pu, C., Eilam, T., Steinder, M., Whally, I., & Snible, E. (2011). Improving performance and availability of services hosted on IaaS clouds with structural constraint-aware virtual machine placement. in services computing (SCC), 2011, IEEE International Conference.
Jayasinghe, D., Pu, C., Eilam, T., Steinder, M., Whally, I., & Snible, E. (2011). Improving performance and availability of services hosted on IaaS clouds with structural constraint-aware virtual machine placement. in services computing (SCC), 2011, IEEE International Conference, (pp. 72-79).
Jin, H., Pan, D., Xu, J., & Pissinou, N. (2012). Efficient VM Placement with multiple deterministic and stochastic resources in data centers . in Global Communications Conference (GLOBECOM) (pp. 2505-2510). IEEE.
Johnston, S. (2009, mar 3). wikipedia:OmniGroup's OmniGraffle and Inkscape (includes Computer.svg by Sasa Stefanovic). Retrieved from wikipedia: https://en.wikipedia.org/wiki/Cloud_computing#/media/File:Cloud_computing.svg
Kantarci, B., Foschini, L., Corradi, A., & Mouftah, H. T. (2012). Inter-and-Intra Data center VM-placement for Energy-Efficient Large-Scale Cloud Systems. First International Workshop on Management and Security technologies for Cloud Computing, (pp. 708-713).
Kaur, G., & Bhardwaj, V. (2016, May 5). A review on VM Placement Strategies. International Journal of Advanced Research in Computer Science and Software Engineering, Vol.6(5), 521-526.
Khanchi, M., & Tyagi, S. (2016, december). VM Scheduling in Cloud Computing using Meta-heuristic Approaches. International Journal of Scientific & Engineering Research, Volume 7(Issue 12), 139-144.
Kord, N., & Haighighi, H. (2013). An Energy-efficient aproach for virtual machine placement in the cloud based datacenters. in information and knowledge Technology (IKT). 2013 5th conference on IEEE.
Le, K., Bianchini, R., Zhang, J., Jaluria, Y., Meng, J., & Nguyen, T. D. (2011). Reducing Electricity cost through virtual machine placement in high performance computing cloude. in proceedings of 2011 Inernational Conference for High Performance Computing, Networking, Storage and Analysis (p. 22). ACM.
Li, K., Wu, J., & Blaisse, A. (2013). Elasticity-aware virtual machine placement for cloud datacenters. in Cloud Networking (CloudNet) , 2013 IEEE 2nd International conference (pp. 99-107). IEEE.
Li, K., Zheng, H., Wu, J., & Du, X. (2015). Virtual machine placement in cloud systems through migration process. International Journal of Parallel, Emergent and Distributed Systems, 393-410.
Liua, X.-F., Zhana, Z.-H., Dub, K.-J., & Chenc, W.-N. (2014). Energy Aware Virtual Machine Placement Scheduling in Cloud Computing Based on Ant Colony Optimization Approach. ACM. Retrieved from http://dx.doi.org/10.1145/2576768.2598265
López-Pires, Barán, B., Pereira, C., Velázquez, M., & González, O. (2019). Evaluation of two phase virtual machine placement algorithms for green cloud datacenters. 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (pp. 62-67). IEEE. doi:10.1109/FAS-W.2019.00028
Lopez-Pires, F., Baran, B., Amarilla, A., Benítez, L., Ferreira, R., & Zalimben, S. (2016). An experimental comparison of algorithms for virtual machine placement considering many objectives. in: 9th Latin America Networking Conference, LANC,, (pp. 75-79).
lopez-pires, F., Baran, B., Benitez, L., Zalinmben, S., & Amarilla, A. (2017). Virtual machine placement for elastic infrastructures in overbooked cloud computing datacenters under uncertainty. Future Generation Computer Systems.
López-Pires, F., Barán, B., Pereira, C., Velázquez, M., & González, O. (2018). Towards Elastic Virtual Machine Placement in Overbooked OpenStack Clouds under Uncertainty. VI Jornadas de Cloud Computing & Big Data (JCC&BD 2018), 146-157.
MamtaKhanchi, & Tyagi, S. (2016). VM Scheduling in Cloud Computing using Meta-heuristic Approaches. International Journal of Scientific & Engineering Research, 7(12), 139-144.
Mark, C. T., Niyato, D., & Chen-Khong, T. (2011). Evolutionary Optimal virtual machine placement and Demand forecaster for Cloud Computing. International Conference on Advanced Information Networking and Aplications, (pp. 348-355).
Masdari, M., Nabavi, S. S., & Ahmadi, V. (2016). An Overview of virtual machine placement schemes In Cloud Computing. Journal of Network and Computer Applications.
McKenzie, E., & Jr, E. S. (2010). Damped trend exponential smoothing: A modelling viewpoint. International Journal of Forecasting, 661–665. doi:10.1016/j.ijforecast.2009.07.001
Mell, P., & Grance, T. (2009). The NIST Defination of Cloud Computing . National Institute of Standards and Technology.
Monil, M. A., & Rahman, R. M. (2016). VM consolidation approach based on heuristics, fuzzy logic and migration control. Journal of Cloud Computing: Advances, Systems and applications.
Ortigoza, J., Pires, F., & Baran, B. (2016). Workload generation for virtual machine placement in cloud computing environments. XLII Latin American Computing Conference,CLEI, (pp. 1-9).
Paliwal, S. (2014). performance challenges in Cloud computing.
Paliwal, S. (n.d.). performance challenges in cloud computing.
Pires, F. L., & Baran, B. (2013). 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.
Pires, F. L., & Baran, B. (2014). virtual machine placement literature review (data). Polytechnic School, National University of Asuncion on, Tech. Rep.
Pires, F. L., & Baran, B. (2015). A Virtual Machine Placement Taxonomy. International Sympodium on Cluster, Cloud and Grid Computing, 15th IEEE/ACM, 159-168.
Prevost, J., Nagothu, K., Kelly, B., & Jamshidi, M. (2013). Optimal update frequency model for physical machine state change state and virtual machine placement in cloud. System of Systems Engineering (SoSE), 8th International Conference on IEEE, (pp. 159-164).
Rao, D. S., & Schwan, K. (2010). vNUMA-mgr : Managing VM Memory on NUMA Platforms. IEEE, 1-10.
Rochwerger, B., Breitgand, D., Levy, E., Galis, A., Nagin, K., Llorente, I., . . . al., J. e. (2009). The reservior model and architechture for open federated cloud computing. IBM Journal of Research and Development,, vol. 53, 1-4.
Shahin, A. A. (2016). Using Multiple Seasonal Holt-Winters Exponential Smoothing to Predict Cloud Resource Provisioning. International Journal of Advanced Computer Science and Applications (IJACSA), Vol,7(No. 11), 91-96.
Shi, L., Furlong, J., & Wang, R. (2013). Empirical evaluation of vector bin packing algorithms for energy efficient data centers. Computers and Communications, ISCC, 000009–000015.
Speitkamp, B., & Bichler, M. (2010). A mathematical programming approach for server. IEEE Trans. Serv. Comput, 266–278.
Tchernykh, A., Schwiegelsohn, U., Alexandrov, V., & Talbi, E. (2015). towards understanding uncertainty in cloud computing resource provisioning. Procedia Comp. Sci., 1772-1781.
Tighe, M., & Bauer, M. (2014). M. Tighe, M. Bauer, Integrating cloud application autoscaling with dynamic VM allocation. in: 2014 IEEE Network Operations and Management Symposium, NOMS, 1-9.
Tomas, L., & Tordsson, J. (2014). An autonomic approach to risk-aware datacenter overbooking. IEEE Trans. Aloud Comput., (pp. 292-305).
Tsai, M.-H., Chou, J., & Chen, J. (2013). prevent VM migration in virtualized clusters via deadline driven placement policy. in Cloud Computing Technology and science (CloudCom), 2013 IEEE 5th International Conference on, vol. 1 (pp. 599-606). IEEE.
Tychinsky, A. (n.d.). Innovation Management of Companies:Mordern approaches, algorithms, experience. Taganrog:Taganrog Institute of Technology. Retrieved from Online Book: http://www.aup.ru/books/m87/
Venigella, S. (2010). Cloud storage and online bin packing, UNLV. Papers/Capstones. Retrieved 4 22, 2015, from Paper 894. http://digitalscholarship.unlv.edu/thesesdissertations/894.
Vijaypal, R. S., Pateriya, S. R., & Rajveer, K. G. (2015). An Efficient Virtual Machine Scheduling Techniques in Cloud Computing Environment. I.J. Mordern Education and Computer Science, 39-46.
Wang, S., Liu, Z., Zheng, Z., Sun, Q., & Yang, F. (2013). particle swarm otimization for energy-aware virtual machine placement optimization in virtualized data centers. In Parallel and Distributed Systems (ICPADS), 2013 International Conference on IEEE (pp. 102-109). IEEE.
Wu, G., Tang, M., Tian, Y.-C., & Li, W. (2012). Energy Efficient virtual machine placement in data centers by Genetic Algorithm. in Neural Information Processing. Springer, 315-323.
Wu, J.-J., Liu, P., & Yang, J.-S. (2012). Workload characteristics-aware virtual machine consolidation algorithms. in proceedings of the 2012 IEEE 4th International Conferencevon Cloud Computing Technology and Science (CloudCom) (pp. 42-49). IEEE Comuter Society.
Xu, J., & Fortes, J. A. (2010). Multi-objective virtual machine placement in virtualized data center environments. IEEE/ACM Int'l conference on & int'l Conference on Cyber, physical and social Computing (CSPCom) (pp. 179-188). in Green Computing and Comunications (GreenCom).
Yahaya, R. H., & Ambursa, F. U. (2019). Enhanced Two-Phase Virtual Machine Placement Scheme for Cloud Computing Datacenters. 2019 15th International Conference on Electronics, Computer and Computation (ICECCO) (pp. 1-5). Abuja, Nigeria: IEEE. doi:10.1109/ICECCO48375.2019.9043260
Yue, W., & Chen, Q. (2014). Dynamic Placement of Virtual Machines with Both Deterministic and Stochastic Demands for green cloud computing. Mathematical Problems in Engineering, pp. 1-11. Retrieved from http://dx.doi.org/10.1155/2014/613719
Yunchi, X., & Shetty, S. (2015). Enabling Security-aware virtual machine placement in IaaS clouds. IEEE Military Communication Conference (Milcon), (pp. 1554-1559).
Zhan, Z. h., Liu, X. f., Gong, Y., & Zhang, J. (2015). Cloud Computing Resource Scheduling and a Survey of its Evolutionary approaches. ACM, 63:1-63:33.
Zhang, X., Zhang, Y., Chen, X., Liu, K., Huang, G., & Zhan, J. (2013). A relationship-based VM placement framework of cloud environment. in proceedings of the 2013 IEEE 37th Annual Computer Software and Applications Conference.
Zheng, Q., Li, R., Li, X., Shah, N., Zhang, J., Tian, F., . . . Li., J. (2016). virtual machine consolidated placement based on multi-objective biogeography-based optimization. Future Gener. Comput. Syst. , 54 , 95–122.
Zheng, X., Yue, Q., & He, Z. (2014). Dynamic energy-efficient virtual machine placement otimization for virtualized clouds. in proceedings of the 2013 International Conference on Electrical and Information Technology for Rail Transportation (EITRT2013).vol. II, pp. 439-448. Springer.
How to Cite
Rahimatu Hayatu Yahaya, Faruku Umar Ambursa, & Bashir Galadanci. (2020). A Two Phase Optimization Scheme with Efficient Prediction-based Triggering for Virtual Machine Placement in Cloud Datacenters. Ilorin Journal of Computer Science and Information Technology, 3(1), 40 - 51. Retrieved from https://iljcsit.com.ng/index.php/ILJCSIT/article/view/33