Abstract
Cloud computing has emerged as a dominant and transformational paradigm in Information technology domain over the last few years. It begins to affect a multitude of industries such as government, finance, telecommunications, and education. The Quality of Service (QoS) of a cloud service provider is an important research field which encompasses different critical issues such as efficient load balancing, response time optimization, completion time improvement, makespan improvement, and reduction in wastage of bandwidth, accountability of the overall system. This paper highlights a new cloudlet allocation policy with suitable load balancing technique that helps in distributing the cloudlets to the virtual machines (VMs) equally likely to their capacity which makes the system more active, alive, and balanced. This reduces the completion time of the cloudlet(s) as well as reduces the makespan of the VM(s) and the host(s) of a data center. Eventually, this proposed work improves the QoS. The experimental results obtained using CloudSim 3.0.3 toolkit extending few base classes are compared and analyzed with several existing allocation policies.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Xiong, K.; Perros, H.: Service performance and analysis in cloud computing. 978-0-7695- 3708-5/09 $25.00 © 2009 IEEE pp. 693–700
Sotomayor, B.; Montero, R.S.; Llorente, I.M.; Foster, I.: Virtual infrastructure management in private and hybrid clouds. 1089-7801/09/$26.00 © 2009 IEEE
Adhikari, M.; Banerjee, S.; Biswas, U.: “Smart task assignment model for cloud service provider” Special Issue of International Journal of Computer Applications (0975–8887) on Advanced Computing and Communication Technologies for HPC Applications - ACCTHPCA, (June 2012)
Lei, X.; Zhe, X.; Shaowu, M.; Xiongyan, T.: Cloud Computing and Services Platform Construction of Telecom Operator. In: Broadband Network & Multimedia Technology, 2009. IC-BNMT ’09. 2nd IEEE International Conference on Digital Object Identifier, pp. 864 – 867
Calheiros, R.N.; Ranjan, R.; De Rose, C.A.F.; Buyya, R.: CloudSim: a novel framework for modelling and simulation of cloud computing infrastructures and services (2009)
Armbrust, M.; Fox, A.; Griffith, R.; Joseph, A.; Katz, R.; Konwinski, A.; Lee, G.; Patterson, D.; Rabkin, A.; Stoica, I.; Zaharia, M.: A Berkeley view of cloud computing. Technical Report No. UCB/EECS-2009-28, University of California at Berkley, USA, Feb. 10, 2009
Aymerich, F.M.; Fenu1, G.; Surcis, S.: An approach to a cloud computing network. 978-1-4244-2624- 9/08/$25.00 ©2008 IEEE 113 pp. 113-118
Buyya, R.; Ranjan, R.; Calheiros, R.N.: Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: challenges and opportunities. In: Proceedings of the 7th High Performance Computing and Simulation Conference (HPCS 2009, ISBN: 978-1-4244-4907-1, IEEE Press, New York, USA), Leipzig, Germany, June 21–24, 2009
White Paper-VMware Infrastructure Architecture Overview, VMware
Ravimaran S., MalukMohamed M.A.: Integrated Obj_FedRep: evaluation of surrogate object based mobile cloud system for Federation, Replica and Data Management. Arab. J. Sci. Eng. 39, 4577–4592 (2014). doi:10.1007/s13369-014-1001-2
Bhatia, W.; Buyy, R.; Ranjan, R.: CloudAnalyst: a CloudSim based visual modeller for analysing cloud computing environments and applications. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 446-452, (2010)
El-kenawy, E.S.T.; El-Desoky, A.I.; Al-rahamawy, M.F.: Extended max–min scheduling using petri net and load balancing. Int. J. Soft Comput. Eng. (IJSCE) 2(4), 198–203 (2012)
Amalarethinam, D.I.G.; MalaiSelvi, F.K.: A minimum makespan grid workflow scheduling algorithm. 978-1-4577-1583-9/ 12/ $26.00 © 2012 IEEE
Syed Abudhagir U., Shanmugavel S.: A novel dynamic reliability optimized resource scheduling algorithm for grid computing system. Arab. J. Sci. Eng. 39, 7087–7096 (2014). doi:10.1007/s13369-014-1305-2
Wee K., Mardeni R., Tan S.W., Lee S.W.: QoS prominent bandwidth control design for real-time traffic in IEEE 802.16e broadband wireless access. Arab. J. Sci. Eng. 39, 2831–2842 (2014). doi:10.1007/s13369-013-0931-4
Brucker P.: Scheduling algorithms, Fifth Edition. Springer Press, New York (2007)
Chatterjee, T.; Ojha, V.K.; Adhikari, M.; Banerjee, S.; Biswas, U.; Snasel, V.: Design and Implementation of a new Datacenter Broker policy to improve the QoS of a Cloud. In: © Springer International Publishing Switzerland 2014, Proceedings of ICBIA 2014, Advances in Intelligent Systems and Computing, vol. 303, pp 281-290 (2014). doi:10.1007/978-3-319-08156-4_28
Ren, X.; Lin, R.; Zua, H.: A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast. In: Proceeding of IEEE CCIS2011, 978-1-61284-204-2/11/$26.00 ©2011 IEEE
A practice of dynamic network load balancingcluster [EB/OL]. http://www.linuxaid.com.cn/articles/1/4/14251644.shtml
Chou, T.-S.: Security threats on cloud computing vulnerabilities. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) (2013). doi:10.5121/ijcsit.2013.5306
Ajith Singh, N.; Hemalatha, M.: An approach on semi distributed load balancing algorithm for cloud computing system. Int. J. Comput. Appl. 56(12), 5–10 (2012)
Alakeel, A.M.: A guide to dynamic load balancing in distributed computer system. Int. J. Comput. Sci. Netw. Secur. 10(6), 153–160 (2010)
Quansheng G., Jiwu S., Xiping M.: Design and implementation of dynamic balance load based on LVS system. Comput. Res. Develop. 41(16), 923–929 (2004)
Adbelzaher, T.F., Bhatti, N.: Web server QoS management by adaptive content delivery[C]. International Workshop on Quality of Service, London, UK (1999)
George Amalarethinam D.I., Muthulakshmi P.: An overview of the scheduling policies and algorithms in Grid Computing. Int. J. Res. Rev. Comput. Sci. 2(2), 280–294 (2011)
Mohammad Khanli, L.; Analoui, M.: Resource scheduling in desktop grid by grid-JQA. In: The 3rd International Conference on Grid and Pervasive Computing, IEEE, 2008
Ghalem, B.; Fatima Zohra, T.; Wieme, Z.: Approaches to improve the resources management in the simulator CloudSim. In: ICICA 2010, LNCS 6377, pp. 189–196, (2010). doi:10.1007/978-3-642-16167-4_25
Calheiros, R.N.; Ranjan, R.; Beloglazov, A.; De Rose, C.A.F.; Buyya, R.: CloudSim: a toolkit for modelling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Published online 24 August 2010 in Wiley Online Library (wileyonlinelibrary.com). doi:10.1002/spe.995
Rawat, P.S.; Saroha, G.P.; Barthwal, V.: Quality of service evaluation of SaaS modeler (Cloudlet) running on virtual cloud computing environment using CloudSim. Int. J. Comput. Appl. 53(13), 35–38 (2012)
Gulati, A.; Chopra, R.K.: Dynamic round robin for load balancing in a cloud computing. IJCSMC, 2(6), 274–278 (2013). ISSN 2320–088X
Parsa, S.; Entezari-Maleki, R.: RASA: a new grid task scheduling algorithm. Int. J. Digit. Content Technol. Appl. 3, 91–99 (2009)
Makespan: http://www2.informatik.huberlin.de/alcox/lehre/lvws1011/coalg/makespan_scheduling.pdf
Campbell, D.T.; Stanley, J.C.: Experimental and quasi-experimental designs for research, Handbook of Research on Teaching, Copyright © 1963 by Houghton Mifflin Company, ISBN: 0-395-30787-2 Y-BBS-IO 09 08
Khadka, Ravi.; Saeidi, Amir.; Idu, Andrei.; Hage, Jurrian.; Jansen, Slinger.: Legacy to SOA evolution: a systematic literature review. Technical Report UU-CS-2012-006, March 2012, Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands, ISSN: 0924-3275
Mattamadugu, L.N.S.; Pathan, A.A.K.: Supercomputing over Cloud using Quicksort algorithm. Master’s Thesis, Electrical Engineering,June 2012, School of Computing Blekinge Institute of Technology, SE—371 79. Karlskrona,Sweden
Calheiros, R.N.; Ranjan, R.; De Rose, C.A.F.; Buyya, R.: CloudSim: a novel framework for modeling and simulation of cloud computing infrastructures and services. Technical Report, GRIDS-TR-2009-1, Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia, 2009
Mahajan, K.; Makroo, A.; Dahiya, D.: Round Robin with server affinity: a VM load balancing algorithm for cloud based infrastructure. J. Inf. Process. Syst. 9(3) (2013). doi:10.3745/JIPS.2013.9.3.379. pISSN 1976-913X
Elgedawy I.: NASEEB: an Escrow-based approach for ensuring data correctness over global clouds. Arab. J. Sci. Eng. 39(12), 8743–8764 (2014). doi:10.1007/s13369-014-1427-6
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Banerjee, S., Adhikari, M., Kar, S. et al. Development and Analysis of a New Cloudlet Allocation Strategy for QoS Improvement in Cloud. Arab J Sci Eng 40, 1409–1425 (2015). https://doi.org/10.1007/s13369-015-1626-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-015-1626-9