A Load Balancing Game Approach for VM Provision Cloud Computing Based on Ant Colony Optimization

1. Gary, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-completeness. WH Freeman and Company, New York (1979)

   Google Scholar 

2. Morton, T., Pentico, D.W.: Heuristic Scheduling Systems: With Applications to Production Systems and Project Management. Wiley, New York (1993)

   Google Scholar 

3. Van Laarhoven, P.J., Aarts, E.H., Lenstra, J.K.: Job shop scheduling by simulated annealing. Oper. Res. 40, 113–125 (1992)

   Article  MathSciNet  MATH  Google Scholar 

4. Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M.: Ant system for job-shop scheduling. Belg. J. Oper. Res. Stat. Comput. Sci. 34, 39–53 (1994)

   MATH  Google Scholar 

5. Ghumman, N.S., Kaur, R.: Dynamic combination of improved max-min and ant colony algorithm for load balancing in cloud system. In: 2015 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–5. IEEE (2015)

   Google Scholar 

6. Tsai, C.-W., Rodrigues, J.J.: Metaheuristic scheduling for cloud: a survey. IEEE Syst. J. 8, 279–291 (2014)

   Article  Google Scholar 

7. Grosu, D., Chronopoulos, A.T., Leung, M.-Y.: Load balancing in distributed systems: an approach using cooperative games. In: Proceedings of the IEEE-IEE Vehicle Navigation and Information Systems Conference 1993, p. 10. IEEE (1993)

   Google Scholar 

8. Grosu, D., Chronopoulos, A.T.: Noncooperative load balancing in distributed systems. J. Parallel Distrib. Comput. 65, 1022–1034 (2005)

   Article  MATH  Google Scholar 

9. Aote, S.S., Kharat, M.: A game-theoretic model for dynamic load balancing in distributed systems. In: Proceedings of the International Conference on Advances in Computing, Communication and Control, pp. 235–238. ACM (2009)

   Google Scholar 

10. Minarolli, D., Freisleben, B.: Utility-based resource allocation for virtual machines in cloud computing. In: 2011 IEEE Symposium on Computers and Communications (ISCC), pp. 410–417. IEEE (2011)

    Google Scholar 

11. Yang, C.-T., Cheng, H.-Y., Huang, K.-L.: A dynamic resource allocation model for virtual machine management on cloud. In: Kim, T.-h., Adeli, H., Cho, H.-s., Gervasi, O., Yau, Stephen, S., Kang, B.-H., Villalba, J.G. (eds.) GDC 2011. CCIS, vol. 261, pp. 581–590. Springer, Heidelberg (2011). doi:10.1007/978-3-642-27180-9_70

    Chapter  Google Scholar 

12. Ye, D., Chen, J.: Non-cooperative games on multidimensional resource allocation. Future Gener. Comput. Syst. 29, 1345–1352 (2013)

    Article  Google Scholar 

13. Xiao, Z., Song, W., Chen, Q.: Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 24, 1107–1117 (2013)

    Article  Google Scholar 

14. Tchernykh, A., Lozano, L., Bouvry, P., Pecero, J.E., Schwiegelshohn, U., Nesmachnow, S.: Energy-aware online scheduling: ensuring quality of service for IaaS clouds. In: 2014 International Conference on High Performance Computing & Simulation (HPCS), pp. 911–918. IEEE (2014)

    Google Scholar 

15. Siar, H., Kiani, K., Chronopoulos, A.T.: An effective game theoretic static load balancing applied to distributed computing. Cluster Comput. 18, 1609–1623 (2015)

    Article  Google Scholar 

16. Liu, L., Mei, H., Xie, B.: Towards a multi-QoS human-centric cloud computing load balance resource allocation method. J. Supercomputing 72, 2488–2501 (2015)

    Article  Google Scholar 

17. Sui, N., Zhang, D., Zhong, W., Wu, L., Zhang, Z.: Evolutionary game theory based network selection for constrained heterogeneous networks. In: 2015 2nd International Conference on Information Science and Control Engineering (ICISCE), pp. 738–742. IEEE (2015)

    Google Scholar 

18. Seddigh, M., Taheri, H., Sharifian, S.: Dynamic prediction scheduling for virtual machine placement via ant colony optimization. In: 2015 Signal Processing and Intelligent Systems Conference (SPIS), pp. 104–108. IEEE (2015)

    Google Scholar 

19. Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., Gu, Z.: Online optimization for scheduling preemptable tasks on IaaS cloud systems. J. Parallel Distrib. Comput. 72, 666–677 (2012)

    Article  Google Scholar 

20. Rahman, M., Li, X., Palit, H.: Hybrid heuristic for scheduling data analytics workflow applications in hybrid cloud environment. In: 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), pp. 966–974. IEEE (2011)

    Google Scholar 

21. Saovapakhiran, B., Michailidis, G., Devetsikiotis, M.: Aggregated-DAG scheduling for job flow maximization in heterogeneous cloud computing. In: 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011), pp. 1–6. IEEE (2011)

    Google Scholar 

22. Osborne, M.J., Rubinstein, A.: A Course in Game Theory. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

23. Pendharkar, P.C.: Game theoretical applications for multi-agent systems. Expert Syst. Appl. 39, 273–279 (2012)

    Article  Google Scholar 

24. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 26, 29–41 (1996)

    Article  Google Scholar 

25. Stützle, T., Hoos, H.H.: MAX–MIN ant system. Future Gener. Comput. Syst. 16, 889–914 (2000)

    Article  MATH  Google Scholar 

26. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1, 53–66 (1997)

    Article  Google Scholar 

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