Abstract
Cloud computing offers on-demand access to a shared set of resources over the internet at lower cost. The advantage of cloud resources is that it can be easily provisioned, configurable, and managed with minimal management efforts. Proper load balancing is an important task in maintaining fault tolerance and Quality of Service (QoS) in the cloud. A load balancer accepts incoming user requests and distributes this workload across multiple Virtual Machines (VMs) using various methods. In a single load balancer system, if the load balancer is down none of the user tasks can’t be processed, even when the servers are ready to process it. This paper proposes a model that will avoid the single point of failure by using multiple load balancers. In this method, service of one load balancer is borrowed or shared among other load balancers when any correction is needed in the estimation of load. This improves fault tolerance of the cloud eco system and assists in cluster capacity management.
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References
Chandakanna, V.R., Vatsavayi, V.K.: AQoS-aware self-correcting observation based load balancer. J. Syst. Softw. 115, 111–120 (2016)
Ould Dey, M.M., Slimani, Y.: Load Balancing approach for QoS management of multi-instance applications in Cloud. In: International Conference on Cloud Computing and Big Data, pp. 119–126 (2013)
Gilly, K., Alcaraz, S., Juiz, C., Puigjaner, R.: Service differentiation and QoS in a scalable content-aware load balancing algorithm. In: Proceedings of the 40th Annual Simulation Symposium (2007)
Zhang, J., Liu, Q.: A multi-agent based load balancing framework in Cloud Environment. In: 9th International Symposium on Computational Intelligence and Design, pp. 278–281 (2016)
Kaur, R., Luthra, P.: Load balancing in cloud system using max min and min-min algorithm. In: International Journal of Computer Applications (0975–8887). National Conference on Emerging Trends in Computer Technology (NCETCT-2014), pp. 31–34 (2014)
Chandakanna, V.R., Vatsavayi, V.K.: Sliding window based Self Learning and Adaptive Load balancer. The journal of system and software 115, 188–205 (2015)
Pius, S.V., Suresh, S.: A novel algorithm of load balancing in distributed file system for cloud. In: IEEE Sponsored 2nd International Conference on Innovations in Information, Embedded and Communication systems (ICIIECS) (2015)
Milani, A.S., Navimipour, N.J.: Review: load balancing mechanisms and techniques in the cloud environments: systematic literature review and future trends. J. Netw. Comput. Appl. 71, 86–98 (2016)
Remesh Babu, K.R., Samuel, P.: Enhanced Bee Colony Algorithm for efficient load balancing and scheduling in cloud. In: Proceedings of the 6th International Conference on Innovations in Bio-Inspired Computing and Applications. IBICA 2015 held in Kochi, India, 16–18 December 2015, pp. 67–78. Springer International Publishing, Cham (2016)
Nguyen, V.H., Khaddaj, S., Hoppe, A., Oppong, E.: A QoS based load balancing framework for large scale elastic distributed systems. In: 10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, pp. 14–15 (2011)
Fu, X., Zhu, X.-X., Han, J., Wang, R.: QoS-aware replica placement for data intensive applications. J. China Univ. Posts Telecommun. 20(3), 43–47 (2013)
Nadap, A., Maral, V.: Methodical analysis of various balancer conditions on public cloud division. In: International Conference on Computing Communication Control and Automation, pp. 40–46 (2015)
Wang, W., Casale, G.: Evaluating weighted round robin load balancing for cloud web services. In: 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pp. 393–400 (2014)
Dasgupta, K., Mandal, B., Dutta, P., Mandal, J.K.: A Genetic Algorithm (GA) based load balancing strategy for cloud computing. In: International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA), vol. 10, pp. 340–347 (2013)
Shen, H.: RIAL: resource intensity aware load balancing in clouds. IEEE Trans. Cloud Comput. 99, 1294–1302 (2014). https://doi.org/10.1109/tcc.2017.2737628
Agnihotri, M., Sharma, S.: Execution analysis of load balancing particle swarm optimization algorithm in cloud data center. In: 2016 Fourth International Conference Parallel, Distributed and Grid Computing (PDGC), vol. 22, pp. 668–672 (2016)
Gupta, A., Garg, R.: Load balancing based task scheduling with ACO in cloud computing. In: 2017 International Conference IEEE Computer and Applications (ICCA), pp. 174–179 (2017)
Wang, H., Ding, L., Wu, P., Pan, Z., Liu, N., You, X.: QoS-Aware load balancing in 3GPP long term evolution multi-cell networks. In: IEEE International Conference IEEE Communications (ICC) (2011)
Rangisetti, A.K., Tamma, B.R.: QoS aware load balance in software defined LTE networks. Elsevier Comput. Commun. 97, 52–71 (2017)
Govindaraju, Y., Duran-Limon, H.: A QoS and energy aware load balancing and resource allocation framework for IaaS cloud providers. In: IEEE/ACM 9th International Conference IEEE Utility and Cloud Computing (UCC), pp. 410–415 (2016)
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Sreelekshmi, S., Remesh Babu, K.R. (2018). Fault Tolerant Multiple Synchronized Parallel Load Balancing in Cloud. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_2
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DOI: https://doi.org/10.1007/978-3-319-76351-4_2
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