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A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment

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Abstract

Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results.

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References

  1. Vijayakumar, K., Arun, C.: Automated risk identification using NLP in cloud based development environments. J. Ambient Intell. Humaniz. Comput. ISSN 1865-5137 (Print), 1868-5145 (Electronic, May 2017)

  2. Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431. Lawrence Berkeley National Laboratory (2008)

  3. Mei, J., Li, K., Li, K.: Energy-aware task scheduling in heterogeneous computing environments. J. Clust. Comput. 17(2), 537–550 (2014)

    Article  Google Scholar 

  4. Shang, L., Peh, L.-S., Jha, N.K.: Dynamic voltage scaling with links for power optimization of interconnection networks. In: Proceedings of the 9th International Symposium on High-Performance Computer Architecture Table of Contents (2003)

  5. Rosoff, M.: Here’s why cloud computing is so hot right now (2011)

  6. Liu, J., Zhao, F., Liu, X., He, W.: Challenges towards elastic power management in Internet data centers. In: WCPS 2009, in Conjunction with ICDCS 2009, June, Montreal, Quebec, Canada (2009)

  7. Gartner Group. http://www.gartner.com/ (2011)

  8. Shen, Q., Liang, X.: Exploiting geo-distributed clouds for a E-health monitoring system with minimum service delay and privacy preservation. IEEE J. Biomed. Health Inform. 18(2), 430–439 (2014)

    Article  Google Scholar 

  9. Wang, L., Khan, S.U.: Review of performance metrics for green data centers: a taxonomy study. J. Supercomput. doi:10.1007/s11227-011-0704-3 (2011)

  10. Kolodziej, J., Khan, S.U.: Data scheduling in data grids and data centers: a short taxonomy of problems and intelligent resolution techniques. In: LNCS Transactions on Computational Collective Intelligence. Springer, Berlin (2011)

  11. Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The Bees Algorithm—a novel tool for complex optimisation problems. In: Proceedings of IPROMS 2006 Conference, pp. 454–461 (2006)

  12. Liang, H.: An SMDP-based service model for interdomain resource allocation in mobile cloud networks. IEEE Trans. Veh. Technol. doi:10.1109/TVT.2012.2194748 (2012)

  13. Pouwelse, J., Langendoen, K., Sips, H.: Energy priority scheduling for variable voltage processors. In: International Symposium on Low Power Electronics and Design, pp. 28–33 (2001)

  14. Benini, L., Bogliolo, A., De Micheli, G.: A survey of design techniques for system-level dynamic power management. IEEE Trans. Very Large Scale Integr. Syst. 8(3), 299–316 (2000)

    Article  Google Scholar 

  15. Zhao, M., Figueiredo, R.J.: Experimental study of virtual machine migration in support of reservation of cluster resources. In: International Workshop on Virtualization Technology in Distributed Computing, pp. 1–8. New York, NY, USA. ACM (2007)

  16. Kliazovich, D., Bouvry, P., Khan, S.U.: DENS: data center energy-efficient network-aware scheduling. Clust. Comput. Spec. Issue Green Netw. doi:10.1007/s10586-011-0177-4 (2011)

  17. Brown, R., et al.: Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431. Lawrence Berkeley National Laboratory, Berkeley (2008)

    Google Scholar 

  18. Fan, X., Weber, W.-D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: Proceedings of the ACM International Symposium on Computer Architecture, San Diego, CA (June 2007)

  19. Raghavendra, R., Ranganathan, P., Talwar, V., Wang, Z., Zhu, X.: No “power” struggles: coordinated multi-level power management for the data center. In: APLOS (2008)

  20. Shang, L., Peh, L.-S., Jha, K.N.: Dynamic voltage scaling with links for power optimization of interconnection networks. In: Proceedings of the 9th International Symposium on High-Performance Computer Architecture, Table of Contents (2003)

  21. Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M.Q., Pentikousis, K.: Energy-efficient cloud computing. Comput. J. 53(7), 1045–1051 (2009)

    Article  Google Scholar 

  22. Li, B., Li, J., Huai, J., Wo, T., Li, Q., Zhong, L.: EnaCloud: an energy-saving application live placement approach for cloud computing environments. In: IEEE International Conference on Cloud Computing, Bangalore, India (2009)

  23. Tang, Q., Gupta, S.K.S., Varsamopoulos, G.: Energy-efficient thermal-aware task scheduling for homogeneous high performance computing data centers: a cyber-physical approach. IEEE Trans. Parallel Distrib. Syst. 19(11), 1458–1472 (2008)

    Article  Google Scholar 

  24. Al-Fares, M., Radhakrishnan, S., Raghavan, B., Huang, N., Vahdat, A.: Hedera: dynamic flow scheduling for data center networks. In: Proceedings of the 7th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’10), San Jose, CA, April (2010)

  25. Meng, X., Pappas, V., Zhang, L.: Improving the scalability of data center networks with traffic-aware virtual machine placement. In: IEEE INFOCOM, San Diego, California, March (2010)

  26. Stage, A., Setzer, T.: Network-aware migration control and scheduling of differentiated virtual machine workloads. In: Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing, International Conference on Software Engineering, May. IEEE Computer Society, Washington, DC (2009)

  27. Rastkhadiv, F., Zamanifar, K.: Task scheduling based on load balancing using artificial bee colony in cloud computing environment. Int. J. Adv. Biotechnol. Res. 7(Special Issue-Number 5), 1058–1069 (2016). ISSN 0976-2612, Online ISSN 2278-599X

  28. Karaboga, D., Basturk, B.: Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems. In: IFSA 2007. LNAI, vol. 4529, pp. 789–798 (2007)

  29. Park, J.H., Yang, L.T., Chen, J.: Research trends in cloud, cluster and grid computing. J. Clust. Comput. 16(3), 335–337 (2013)

    Article  Google Scholar 

  30. Liu, J., Zhao, F., Liu, X., He, W.: Challenges towards elastic power management in Internet data centers. In: Proceedings of the 2nd International Workshop on Cyber-Physical Systems (WCPS 2009) in Conjunction with ICDCS 2009, Montreal, Quebec, Canada, June (2009)

  31. Song, Y., Wang, H., Li, Y., Feng, B., Sun, Y.: Multi-tiered on-demand resource scheduling for VM-based data center. In: IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID), May, pp. 148–155 (2009)

  32. Beloglazov, A., Buyya, R.: Energy efficient resource management in virtualized cloud data centers. In: IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), May, pp. 826–831 (2010)

  33. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. doi:10.1016/j.asoc.2007.05.007 (2008)

  34. Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report TR06. Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

  35. Mahmoud, M.M.E.A., Shen, X. (Sherman): A cloud-based scheme for protecting source-location privacy against hotspot-locating attack in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(10), 1805–1818 (2012)

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Muthulakshmi, B., Somasundaram, K. A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment. Cluster Comput 22 (Suppl 5), 10769–10777 (2019). https://doi.org/10.1007/s10586-017-1174-z

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