, Volume 99, Issue 6, pp 575–595 | Cite as

Scalable and automatic virtual machines placement based on behavioral similarities

  • Claudia Canali
  • Riccardo Lancellotti


The success of the cloud computing paradigm is leading to a significant growth in size and complexity of cloud data centers. This growth exacerbates the scalability issues of the Virtual Machines (VMs) placement problem, that assigns VMs to the physical nodes of the infrastructure. This task can be modeled as a multi-dimensional bin-packing problem, with the goal to minimize the number of physical servers (for economic and environmental reasons), while ensuring that each VM can access the resources required in the next future. Unfortunately, the naïve bin packing problem applied to a real data center is not solvable in a reasonable time because the high number of VMs and of physical nodes makes the problem computationally unmanageable. Existing solutions improve scalability at the expense of solution quality, resulting in higher costs and heavier environmental footprint. The Class-Based placement technique (CBP) is a novel approach that exploits existing solutions to automatically group VMs showing similar behaviour. The Class-Based technique solves a placement problem that considers only some representative VMs for each class, and that can be replicated as a building block to solve the global VMs placement problem. Using real traces, we analyse our proposal performance, comparing different alternatives to automatically determine the number of building blocks. Furthermore, we compare our proposal against the existing alternatives and evaluate the results for different workload compositions. We demonstrate that the CBP proposal outperforms existing solutions in terms of scalability and VM placement quality.


Cloud computing Infrastructure as a service Scalability Virtual Machine placement Class-based placement 

Mathematics Subject Classification

90C06 Large-scale problems 90C11 Mixed integer programming 


  1. 1.
    Gantz J, Reinsel D (2012) The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. IDC iView IDC Anal Future 2007:1–16Google Scholar
  2. 2.
    EPA (2011) Data center consolidation plan, US Environmental Protection Agency, Tech. RepGoogle Scholar
  3. 3.
    Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. J Internet Serv Appl 1(1):7–18CrossRefGoogle Scholar
  4. 4.
    Setzer T, Bichler M (2013) Using matrix approximation for high-dimensional discrete optimization problems: server consolidation based on cyclic time-series data. Eur J Oper Res 227(1):62–75MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Speitkamp B, Bichler M (2010) A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Trans Serv Comput 3(4):266–278CrossRefGoogle Scholar
  6. 6.
    Rochwerger B, Breitgand D, Epstein A, Hadas D, Loy I, Nagin K, Tordsson J, Ragusa C, Villari M, Clayman S et al (2011) Reservoir—when one cloud is not enough. IEEE Comput 44(3):44–51CrossRefGoogle Scholar
  7. 7.
    Mills K, Filliben J, Dabrowski C (2011) Comparing VM-placement algorithms for on-demand clouds. In: Proc. of IEEE 3rd International Conference on Cloud Computing Technology and Science (CloudCom), Athens, GreeceGoogle Scholar
  8. 8.
    Tang C, Steinder M, Spreitzer M, Pacifici G (2007) A scalable application placement controller for enterprise data centers. In: Proc. of the 16th International Conference on World Wide Web (WWW), Banff, Alberta, CanadaGoogle Scholar
  9. 9.
    Barroso LA, Hölzle U (2007) The case for energy-proportional computing. IEEE Comput 40(12):33–37CrossRefGoogle Scholar
  10. 10.
    Setzer T, Stage A (2010) Decision support for virtual machine reassignments in enterprise data centers. In: Proc. of Network Operations and Management Symposium, Osaka, JapanGoogle Scholar
  11. 11.
    Wäscher G, Haußner H, Schumann H (2007) An improved typology of cutting and packing problems. Eur J Oper Res 183(3):1109–1130CrossRefzbMATHGoogle Scholar
  12. 12.
    Kao M (2008) Encyclopedia of algorithms.Springer-Verlag, New York, IncGoogle Scholar
  13. 13.
    Canali C, Lancellotti R (2013) Automatic virtual machine clustering based on Bhattacharyya distance for multi-cloud systems. In: Proc. of International Workshop on Multi-cloud Applications and Federated Clouds (MultiCloud, Prague, Czech RepublicGoogle Scholar
  14. 14.
    Canali C, Lancellotti R (2014) Improving scalability of cloud monitoring through PCA-based clustering of virtual machines. J Comput Sci Technol 29(1):38–52Google Scholar
  15. 15.
    Canali C, Lancellotti R (2014) An adaptive technique to model virtual machine behavior for scalable cloud monitoring. In: Proc. of IEEE Symposium on Computers and Communications (ISCC), Madeira, PortugalGoogle Scholar
  16. 16.
    Canali C, Lancellotti R (2015) Exploiting classes of virtual machines for scalable IaaS cloud management. In: Proc. of the 4th Symposium on Network Cloud Computing and Applications (NCCA), Munich, GermanyGoogle Scholar
  17. 17.
    Rabinovich M, Spatscheck O (2002) Web caching and replication. Addison-Wesley Boston, USA, BostonGoogle Scholar
  18. 18.
    Iyengar AK, Squillante MS, Zhang L (1999) Analysis and characterization of large-scale Web server access patterns and performance. World Wide Web 2(1–2):85–100CrossRefGoogle Scholar
  19. 19.
    Casolari S, Colajanni M (2010) On the selection of models for runtime prediction of system resources. In: Ardagna D, Zhang L (eds) Run-time models for self-managing systems and applications, ser. Autonomic Systems. Springer, pp. 25–44Google Scholar
  20. 20.
    Mastroianni C, Meo M, Papuzzo G (2013) Probabilistic consolidation of virtual machines in self-organizing cloud data centers. IEEE Trans Cloud Comput 1(2):215–228CrossRefGoogle Scholar
  21. 21.
    Fang W, Liang X, Li S, Chiaraviglio L, Xiong N (2013) VMPlanner: optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers. Comput Netw 57(1):179–196CrossRefGoogle Scholar
  22. 22.
    Zhang R, Routray R, Eyers DM, Chambliss D, Sarkar P, Willcocks D, Pietzuch P (2011) IO Tetris: deep storage consolidation for the cloud via fine-grained workload analysis. In: Proc. of 4th IEEE International Conference on Cloud Computing, (CLOUD), Washington, DC, USAGoogle Scholar
  23. 23.
    Andreolini M, Casolari S, Colajanni M (2008) Models and framework for supporting runtime decisions in Web-based systems. ACM Trans Web 2(3):1–43CrossRefGoogle Scholar
  24. 24.
    Addis B, Ardagna D, Panicucci B, Squillante M, Zhang L (2013) A hierarchical approach for the resource management of very large cloud platforms. IEEE Trans Depend Secure Comput 10(5):253–272CrossRefGoogle Scholar
  25. 25.
    Stillwell M, Schanzenbach D, Vivien F, Casanova H (2010) Resource allocation algorithms for virtualized service hosting platforms. J Parallel Distrib Comput 70(9):962–974CrossRefzbMATHGoogle Scholar
  26. 26.
    Addis B, Ardagna D, Panicucci B, Squillante MS, Zhang L (2013) A hierarchical approach for the resource management of very large cloud platforms. IEEE Trans Depend Secure Comput 10(5):253–272CrossRefGoogle Scholar
  27. 27.
    Epstein L, Favrholdt L, Kohrt J (2012) Comparing online algorithms for bin packing problems. J Sched 15(1):13–21MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Zhang L, Ardagna D (2004) SLA based profit optimization in autonomic computing systems. In: Proc. of the 2nd International Conference on Service Oriented Computing (ICSOC), New York, NY, USAGoogle Scholar
  29. 29.
    Zhu X, Young D, Watson B, Wang Z, Rolia J, Singhal S, McKee B, Hyser C, Gmach D, Gardner R, Christian T, Cherkasova L (2009) 1000 islands: an integrated approach to resource management for virtualized data centers. J Cluster Comput 12(1):45–57CrossRefGoogle Scholar
  30. 30.
    Faroe O, Pisinger D, Zachariasen M (2003) Guided local search for the three-dimensional bin-packing problem. Inf J Comput 15(3):267–283MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Crainic TG, Perboli G, Tadei R (2009) Ts 2 pack: a two-level tabu search for the three-dimensional bin packing problem. Eur J Oper Res 195(3):744–760CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  1. 1.Department of Engineering “Enzo Ferrari”University of Modena and Reggio EmiliaModenaItaly

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