Abstract
Distributed computing is referring to both the applications and the services provided over the web or Internet and the hardware and system programming in data centers that provide its services. The cloud computing gives an expansive pool of shared resources, programming bundles, data, storage, and a wide range of applications according to client requests at any time. Cloud computing is developing rapidly; an extensive number of clients are pulled in toward cloud administrations for more fulfillment. Adjusting the heap has turned out to be an additionally intriguing exploration zone in this field. Better load adjusting calculation in the cloud framework builds the execution and resource use by increasingly dispersing work stack among various nodes in the system. With the approach of a public cloud domain, numerous other solicitations and administrations across geologies that need to run. A few workloads might be changeless and need to run continuously, for example, an online trade webpage or a control framework that deals with basic conservation processing. Virtualized workloads include another level of many-sided quality. This paper aims to investigate the difficulties of load adjusting in the public cloud environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Desai T, Prajapati J (2013) A survey of various load balancing techniques and challenges in cloud computing. Int J Sci Technol Res 2(11):158–161. https://doi.org/10.1.1.637.6719
Wang L, Tao J, Kunze M, Castellanos AC, Kramer D, Karl W (2008) Scientific cloud computing: early definition and experience. In: 2008 10th IEEE international conference on high performance computing and communications, pp 825–830. https://doi.org/10.1109/hpcc.2008.38
Rana M, Bilgaiyan S, Kar U (2014) A study on load balancing in cloud computing environment using evolutionary and swarm based algorithms. In: 2014 international conference on control, instrumentation, communication and computational technologies (ICCICCT), pp 245–250. https://doi.org/10.1109/iccicct.2014.6992964
Foster I, Zhao Y, Raicu I, Lu S (2008) Cloud computing and grid computing 360° compared. arXiv preprint:0901.0131
Buyya R, Ranjan R, Calheiros RN (2010) Intercloud: utility-oriented federation of cloud computing environments for scaling of application services. In: International conference on algorithms and architectures for parallel processing. Springer, Berlin, Heidelberg, pp 13–31. https://doi.org/10.1007/978-3-642-13119-6_2
Tsakalozos K, Roussopoulos M, Floros V, Delis A (2010) Nefeli: hint-based execution of workloads in clouds. In: 2010 IEEE 30th international conference on distributed computing systems, pp 74–85. https://doi.org/10.1109/icdcs.2010.66
Kousiouris G, Cucinotta T, Varvarigou T (2011) The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks. J Syst Softw 84(8):1270–1291. https://doi.org/10.1016/j.jss.2011.04.013
Ray S, De Sarkar A (2012) Execution analysis of load balancing algorithms in cloud computing environment. Int J Cloud Comput: Serv Archit (IJCCSA) 2(5):1–13. https://doi.org/10.5121/ijccsa.2012.2501
Katoch S, Thakur J (2014) Load balancing algoritms in cloud computing environment: a review. Int J Recent Innov Trends Comput Commun 2(8):2151–2156. https://doi.org/10.26438/ijcse/v6i8.771778
El-Gazzar RF (2014) An overview of cloud computing adoption challenges in the Norwegian context. In: 2014 IEEE/ACM 7th international conference on utility and cloud computing, pp 412–418. https://doi.org/10.1109/ucc.2014.52
Rajput SS, Kushwah VS (2016) A review on various load balancing algorithms in cloud computing. J Eng Appl Sci 6(4):8579–8585. https://doi.org/10.3923/jeasci.2017.8579.8585
More NS, Hiray SR (2012) Load balancing and resource monitoring in cloud. In: Proceedings of the CUBE international information technology conference, pp 552–556. https://doi.org/10.1145/2381716.2381821
George Almubaddel M, Elmogy, AM (2016) Cloud computing antecedents, challenges, and directions. In: Proceedings of the international conference on internet of things and cloud computing, article no 16. https://doi.org/10.1145/2896387.2896401
Lenk A, Klems, M, Nimis J, Tai S, Sandholm T (2009) What’s inside the cloud? An architectural map of the cloud landscape. In: 2009 ICSE workshop on software engineering challenges of cloud computing, pp 23–31. https://doi.org/10.1109/cloud.2009.5071529
Sotomayor B, Montero RS, Llorente IM, Foster I (2009) Virtual infrastructure management in private and hybrid clouds. In: IEEE Internet Comput 13(5):14–22. https://doi.org/10.1109/mic.2009.119
Somani R, Ojha J (2014) A hybrid approach for VM load balancing in cloud using cloudsim. Int J Sci Eng Technol Res (IJSETR) 3(6):1734–1739. http://ijsetr.org/wp-content/uploads/2014/06/IJSETR-VOL-3-ISSUE-6-1734-1739.pdf
Gupta R (2014) Review on existing load balancing techniques of cloud computing. Int J Adv Res Comput Sci Softw Eng 4(2):168–171. http://ijarcet.org/wp-content/uploads/IJARCET-VOL-4-ISSUE-7-3308-3311.pdf
Rajguru AA, Apte SS (2015) Various strategies of load balancing techniques and challenges in distributed systems. Int J Sci Res Eng Technol (IJSRET) 4(7):741–748. http://www.ijsret.org/pdf/121134.pdf
Raghava NS, Singh D (2014) Comparative study on load balancing techniques in cloud computing. Open J Mob Comput Cloud Comput 1(1):18–25. http://ijiset.com/vol3/v3s10/IJISET_V3_I10_42.pdf
Ramesh GP. Performance analysis of traffic with optical broker for load balancing and multicasting in software defined data center networking. http://hdl.handle.net/10603/180771
Gopinat PG, Vasudevan SK (2015) An in-depth analysis and study of load balancing techniques in the cloud computing environment. Procedia Comput Sci 50:427–432. https://doi.org/10.1016/j.procs.2015.04.009
Bakde, KG, Patil BM (2016) Survey of techniques and challenges for load balancing in public cloud. Int J Tech Res Appl 4(2):279–290. https://www.ijtra.com/view/survey-of-techniques-and-challenges-for-load-balancing-in-public-cloud.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Baskar, K., Venkatesan, G.K.D.P., Sangeetha, S. (2020). A Survey of Workload Management Difficulties in the Public Cloud. In: Solanki, V., Hoang, M., Lu, Z., Pattnaik, P. (eds) Intelligent Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1125. Springer, Singapore. https://doi.org/10.1007/978-981-15-2780-7_54
Download citation
DOI: https://doi.org/10.1007/978-981-15-2780-7_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2779-1
Online ISBN: 978-981-15-2780-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)