Hosting Clients in Clustered and Virtualized Environment: A Combinatorial Optimization Approach

  • Yacine LaalaouiEmail author
  • Jehad Al-Omari
  • Hedi Mhalla
Part of the Studies in Computational Intelligence book series (SCI, volume 607)


This paper presents a global approach to deal with the problem of allocating a set of clients to a common pool of multiple clusters based on number of connections to advance resources management in virtual environment. To optimize resources allocation in Applications Services Provider’s data-centers, we propose a combinatorial optimization look to the problem. First, we describe the corresponding integer mathematical model. Then, we use the IBM CPLEX solver to solve to optimally this problem.


Hosting clients Cluster Virtual machine Combinatorial optimization 


  1. 1.
    Barham, P., et al.: Xen and the art of virtualization. In: Proceedings of the 9th ACM symposium on Operating systems principles (SOSP ’03), Bolton Landing, NY, USA, pp. 164–177 (2003)Google Scholar
  2. 2.
    Adams, K., Agesen, O.: A Comparison of software and hardware techniques for x86 virtualization. In: Proceedings of the 12th international conference on Architectural support for programming languages and operating systems (ASPLOS XII), San Jose, California, pp. 2–13 (2006)Google Scholar
  3. 3.
  4. 4.
  5. 5.
    VMWare Inc: VMware infrastructure architecture overview.
  6. 6.
    Kernel-based Virtual Machine.
  7. 7.
    Wang, D., Xie, W.: Performability analysis of clustered systems with rejuvenation under varying workload. Perform. Eval. 64(3), 247–265 (2007)Google Scholar
  8. 8.
    VMware Inc: VMware high availability: concepts, implementation, and best practices.
  9. 9.
    Fox, A., Gribble, S.D., Chawathe, Y., Brewer, E.A., Gauthier, P.: Cluster-based scalable network services. In: Proceedings of the 16th ACM Symposium on Operating Systems Principles, pp. 78–91 (1997)Google Scholar
  10. 10.
    BEA White Paper: Achieving scalability and high availability for e-business, clustering in bea weblogic server. (2003)
  11. 11.
    Garey, M.R., Johnson, D.S.: Computers and intractability. In: A Guide to the Theory of NP-completeness. Freeman, NewYork, USA (1979)Google Scholar
  12. 12.
    Wu, L., Garg S.K., Buyya, R.: SLA-Based resource allocation for software as a service provider (SaaS) in cloud computing environments. In: Proceedings of 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (2011)Google Scholar
  13. 13.
    Nan, X., He, Y., Guan, L.: Optimal resource allocation for multimedia application providers in multi-site cloud. In: Proceedings of 2013 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, pp. 449–452 (2013)Google Scholar
  14. 14.
    Ferretti, S., Ghini, V., Panzieri, F., Pellegrini, M., Turrini, E.: QoS aware clouds. In: Proceedings of IEEE 3rd international conference on cloud computing, pp 321–328 (2010)Google Scholar
  15. 15.
    Nathuji, R., Kansal, A., Ghaffarkhah, A.: Q-clouds: managing performance interference effects for QoS-aware clouds. In: Proceedings of EuroSys’10 of the 5th European conference on Computer systems, pp. 237–250 (2010)Google Scholar
  16. 16.
    Lai, G., Song, H., Lin, X.: A service based light weight desktop virtualization system. In: Proceedings of the International Conference on Service Sciences (ICSS’2010), pp. 277–282 (2010)Google Scholar
  17. 17.
    Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)CrossRefGoogle Scholar
  18. 18.
    Berral, J.L., Gavalda, R., Torres, J.: Adaptive scheduling on power-aware managed data-centers using machine learning. In: Proceedings of 12th IEEE/ACM International Conference on Grid Computing, pp. 66–73 (2011)Google Scholar
  19. 19.
    Martello, S., Toth, P.: Knapsack problems : algorithms and computer implementations. In: Wiley Series in Discrete Mathematics and Optimization, Chapter 8 (1990)Google Scholar
  20. 20.
    Lodi, Andrea, Martello, Silvano, Vigo, Daniele: Recent advances on two-dimensional bin packing problems. Discrete Appl. Math. 123(1–3), 379–396 (2002)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Baruah, S., Fisher, N.: The partitioned multiprocessor scheduling of sporadic task systems. In: RTSS’05 Proceedings of the 26th IEEE International Real-Time Systems Symposium, pp. 321–329 (2005)Google Scholar
  22. 22.
    Cook, J.S., Han, B.T.: Optimal robot selection and workstation assignment for a CIM system. IEEE Trans. Robot. Autom. 10(2), 210–219 (1994)CrossRefGoogle Scholar
  23. 23.
    Han, B.T., Diehr, G.: An algorithm for device selection and file assignment. Eur. J. Oper. Res. 61, 326–344 (1992)CrossRefzbMATHGoogle Scholar
  24. 24.
    Boyer, V., El Baz, D., Elkihel, M.: A dynamic programming method with lists for the knapsack sharing problem. Comput. Ind. Eng. 61, 274–278 (2010)CrossRefGoogle Scholar
  25. 25.
    Hifi, M., MHalla, H., Sadfi, S.: An exact algorithm for the knapsack sharing problem. Comput. Oper. Res. 32, 1311–1324 (2005)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Yamada, T., Futakawa, M., Kataoka, S.: Some exact algorithms for the knapsack sharing problem. Eur. J. Oper. Res. 106, 177–183 (1998)CrossRefGoogle Scholar
  27. 27.
    Brown, J.R.: Solving knapsack sharing with general tradeoff functions. Math. Program. 5, 55–73 (1991)CrossRefGoogle Scholar
  28. 28.
    Kuno, T., Konno, H., Zemel, E.: A linear-time algorithm for solving continuous maximum knapsack problems. Oper. Res. Lett. 10, 23–26 (1991)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Luss, H.: Minmax resource allocation problems: optimization and parametric analysis. Eur. J. Oper. Res. 60, 76–86 (1992)CrossRefzbMATHGoogle Scholar
  30. 30.
    Pang, J.S., Yu, C.S.: A min-max resource allocation problem with substitutions. Eur. J. Oper. Res. 41, 218–223 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Tang, C.S.: A max-min allocation problem: its solutions and applications. Oper. Res. 36, 359–367 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
  33. 33.
    Korf, R.E.: Artificial intelligence search algorithms. In: Atallah, M.J. (ed.) Algorithms and Theory of Computation, Handbook. CRC Press, Boca Raton (1998) (ISBN:0849326494)Google Scholar
  34. 34.
    Fukunaga, Alex S., Korf, Richard E.: Bin completion algorithms for multicontainer packing, knapsack, and covering problems. J. Artif. Intell. Res. (JAIR) 28, 393–429 (2007)MathSciNetzbMATHGoogle Scholar
  35. 35.
    Chandra, A., Gong, W., Shenoy, P.: Dynamic resource allocation for shared data centers using online measurements. In: International conference on Measurement and modeling of computer systems (SIGMETRICS ’03), pp. 300–301 (2003)Google Scholar
  36. 36.
    Waldspurger, C.A.: Memory resource management in VMware ESX server. In: Proceedings of the 5th Symposium on Operating Systems Design and Implementation (OSDI’02), pp. 181–194 (2002)Google Scholar
  37. 37.
    Khanna, G., Beaty, K., Kar, G., Kochut, A.: Application performance management in virtualized server environments. In: Proceedings of 10th IEEE Network Operations and Management Symposium (NOMS), pp. 373–381 (2006)Google Scholar
  38. 38.
  39. 39.
    Chen, Q., Xin, R.: Optimizing enterprise IT infrastructure through virtual server consolidation. In: Proceedings of the 2005 Informing Science and IT Education Joint Conference, Flagstaff, Arizona, USA, June 2005Google Scholar
  40. 40.
    Kshetri, N.: Cloud computing in developing economies. IEEE Comput 43(10), 47–55 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Information Technology, College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia
  2. 2.Department of Mathematics and StatisticsThe American University of the Middle EastEqailaKuwait

Personalised recommendations