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An Approach Towards Development of a New Cloudlet Allocation Policy with Dynamic Time Quantum

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Abstract

Cloud computing is one of the most emerging technologies which has created a revolution in the High performance Computing (HPC) domain. The term Quality of Service (QoS) plays a vital role in the formation of more flexible integration of various technologies. The Waiting Time (WT), Turnaround Time (TAT), Context Switching (CS) and Makespan (MS) are the primary parameter that has great impact on the scheduling of cloudlets. The Proposed algorithm has improved the resource utilization system of the existing Round Robin Algorithm (RRA) and Improved Round Robin Cloudlet Scheduling Algorithm (IRRCSA) by introducing the concept of dynamically calculated Time Quantum (TQ) for each virtual machine (VM) according to the allocated cloudlets. This new approach in cloudlet scheduling drastically reduced average WT, average TAT and Number of CS of the VMs, which further enhanced the capability of cloud service providers (CSPs) to provide better QoS.

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References

  1. Bianco, P., Lewis, G.A., and Merson, P., Service Level Agreements in Service-Oriented Architecture Environments (No. CMU/SEI-2008-TN-021), Carnegie-Mellon Univ. Pittsburgh Pa Software Engineering Inst., 2008. http://www.sei.cmu.edu/.

    Book  Google Scholar 

  2. Wu, X., Deng, M., Zhang, R., Zeng, B., and Zhou, S., A task scheduling algorithm based on QoS-driven in cloud computing, Procedia Comput. Sci., 2013, vol. 17, pp. 1162–1169. https://.doi.org/10.1016/j.procs.2013.05.148

    Article  Google Scholar 

  3. Nayak, D., Malla, S.K., and Debadarshini, D., Improved round robin scheduling using dynamic time quantum, Int. J. Comput. Appl., 2012, vol. 38, no.5.

    Google Scholar 

  4. Rimal, B.P., Choi, E., and Lumb, I., A taxonomy, survey, and issues of cloud computing ecosystems, in Cloud Computing: Principles Systems and Applications, Computer Communications and Networks, Antonopoulos, N. and Gillam, L., Eds., Berlin: Springer, pp. 21–46. doi 10.1007/978-1-84996-241-4_2

  5. Shaw, S.B., and Singh, A.K., A survey on scheduling and load balancing techniques in cloud computing environment, Computer and Communication Technology (ICCCT), 2014 International Conference on, 2014, pp. 87–95.

    Google Scholar 

  6. Buyya, R., Economic-based distributed resource management and scheduling for grid computing, arXiv preprint cs/0204048, 2012.

    Google Scholar 

  7. Wilkins-Diehr, N., Special issue: Science gateways—common community interfaces to grid resources, Concurr. Comput.: Pract. Exp., 2007, vol. 19, no. 6, pp. 743–749.

    Article  Google Scholar 

  8. Belalem, G., Tayeb, F.Z., and Zaoui, W., Approaches to improve the resources management in the simulator CloudSim, International Conference on Information Computing and Applications, 2010, pp. 189–196. doi 10.1007/978-3-642-16167-4_25

    Chapter  Google Scholar 

  9. Dumitrescu, C.L. and Foster, I., GangSim: A simulator for grid scheduling studies, Cluster Computing and the Grid, 2005. CCGrid 2005. IEEE International Symposium on, 2005, vol. 2, pp. 1151–1158.

    Article  Google Scholar 

  10. Legrand, A., Marchal, L., and Casanova, H., Scheduling distributed applications: The SimGrid simulation framework, Cluster Computing and the Grid, 2003. Proceedings. CCGrid 2003. 3rd IEEE/ACM International Symposium on, 2003, pp. 138–145.

    Chapter  Google Scholar 

  11. Calheiros, R.N., Ranjan, R., De Rose, C.A., and Buyya, R., CloudSim: A novel framework for modeling and simulation of cloud computing infrastructures and services, arXiv preprint arXiv:0903.2525, 2009.

    Google Scholar 

  12. Calheiros, R.N., Ranjan, R., De Rose, C.A.F., and Buyya, R., CloudSim: A novel framework for modeling and simulation of cloud computing infrastructures and services, in Technical Report, GRIDS-TR-2009-1, Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia, 2009.

    Google Scholar 

  13. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., and Buyya, R., CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Pract. Exp., 2011, vol. 41, no. 1, pp. 23–50.

    Google Scholar 

  14. Banerjee, S., Adhikari, M., and Biswas, U., Design and analysis of an efficient QoS improvement policy in cloud computing, Serv. Oriented Comput. Appl., 2017, vol. 11, no. 1, pp. 65–73.

    Article  Google Scholar 

  15. El-kenawy, E.S.T., El-Desoky, A.I., and Al-Rahamawy, M.F., Extended max-min scheduling using Petri net and load balancing, Int. J. Soft Comput. Eng., 2012, vol. 2, no. 4, pp. 198–203.

    Google Scholar 

  16. Parsa, S., and Entezari-Maleki, R., RASA: A new task scheduling algorithm in grid environment, World Appl. Sci. J., 2009, vol. 7, pp. 152–160.

    Google Scholar 

  17. Malhotra, R., and Jain, P., Study and comparison of CloudSim simulators in the cloud computing, SIJ Trans. Comput. Sci. Eng. Its Appl., 2013, vol. 1, no. 4, pp. 111–115.

    Google Scholar 

  18. Contributed by techgreek in (2010). Types of scheduling 4th June.

  19. Mohan, S., Mixed scheduling, a new scheduling policy, Proceedings of Insight’09, 2009.

    Google Scholar 

  20. Yang, J., Khokhar, A., Sheikh, S., and Ghafoor, A., Estimating execution time for parallel tasks in heterogeneous processing (HP) environment, Heterogeneous Computing Workshop, 1994, Proceedings, 1994, pp. 23–28. doi 10.1109/HCW.1994.324966

    Google Scholar 

  21. Roy, S., Banerjee, S., Chowdhury, K.R., and Biswas, U., Development and analysis of a three phase cloudlet allocation algorithm, J. King Saud Univ., Comput. Inf. Sci., 2017, vol. 29, no. 4, pp. 473–483.

    Google Scholar 

  22. Bhatia, W., Buyya, R., and Ranjan, R., CloudAnalyst: A CloudSim based visual modeller for analyzing cloud computing environments and applications, 2010 24th IEEE International Conference on Advanced Information Networking and Applications, 2010, pp. 446–452.

    Google Scholar 

  23. Brucker, P., Scheduling Algorithms, Berlin: Springer, 5th ed. doi 10.1007/978-3-540-69516-5

  24. Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., and Warfield, A., Xen and the art of virtualization, ACM SIGOPS Oper. Syst. Rev., 2003, vol. 37, no. 5, pp. 164–177.

    Article  Google Scholar 

  25. Xiong, K. and Perros, H., Service performance and analysis in cloud computing, Services-I, 2009 World Conference on, 2009, pp. 693–700. doi 10.1109/SERVICES-I.2009.121

    Google Scholar 

  26. Sotomayor, B., Montero, R.S., Llorente, I.M., and Foster, I., Virtual infrastructure management in private and hybrid clouds, IEEE Internet Comput., 2009, vol. 13, no. 5. doi 10.1109/MIC.2009.119

    Google Scholar 

  27. Adhikari, M., Banerjee, S., and Biswas, U., Smart task assignment model for cloud service provider, Int. J. Comput. Appl. Adv. Comput. Commun. Technol. HPC Appl., 2012, special issue, pp. 43–46.

    Google Scholar 

  28. Lei, X., Zhe, X., Shaowu, M., and Xiongyan, T., Cloud computing and services platform construction of telecom operator, Broadband Network and Multimedia Technology, 2009. IC-BNMT'09. 2nd IEEE International Conference on, 2009, pp. 864–867. doi 10.1109/ICBNMT.2009.5347793

    Chapter  Google Scholar 

  29. Banerjee, S., Adhikari, M., and Biswas, U., Development of a smart job allocation model for a cloud service provider, Business and Information Management (ICBIM), 2014 2nd International Conference on, 2014, pp. 114–119. doi 10.1109/ICBIM.2014.6970946

    Chapter  Google Scholar 

  30. Banerjee, S., Adhikari, M., Kar, S., and Biswas, U., Development and analysis of a new cloudlet allocation strategy for QoS improvement in cloud, Arabian J. Sci. Eng., 2015, vol. 40, no. 5, pp. 1409–1425. doi 10.1007/s13369-015-1626-9

    Article  MathSciNet  MATH  Google Scholar 

  31. Chen, Z., Xu, G., Mahalingam, V., Ge, L., Nguyen, J., Yu, W., and Lu, C., A cloud computing based network monitoring and threat detection system for critical infrastructures, Big Data Res., 2016, vol. 3, pp. 10–23.

    Article  Google Scholar 

  32. Molyakov, A.S., Zaborovsky, V.S., and Lukashin, A.A., Model of hidden IT security threats in the cloud computing environment, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 741–744.

    Article  Google Scholar 

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Correspondence to Sourav Banerjee.

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Banerjee, S., Chowdhury, A., Mukherjee, S. et al. An Approach Towards Development of a New Cloudlet Allocation Policy with Dynamic Time Quantum. Aut. Control Comp. Sci. 52, 208–219 (2018). https://doi.org/10.3103/S0146411618030033

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  • DOI: https://doi.org/10.3103/S0146411618030033

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