Abstract
Cloud servers or Host machines are having a sufficient number of computing resources, and these resources will be shared among its client applications as per the requirement. The traffic on the cloud servers or Host machines is unpredictable, and the demand may increase or decrease with a great variations many times. Even there are lots of reasons of such variations in traffic, here we are not going to discuss such reasons. During the COVID-19 pandemic, most of the meetings, teaching–learning, documents exchanging, etc. are performed using online applications. It is noticed that the both data and processing load are increased in multiple times during the last six months on the social networking sites, and clients faced the problem of low connectivity and slow processing of applications. These cases show that there is very great requirement of load balancing of computing resources on cloud servers to maintain the smooth processing of tasks. In this paper, our main focus is to measure the impacts of scheduling interval on dynamic resource allocation and to calculate the demand of resources dynamically on the basis of dynamic creation or deletion of virtual machines to serve the cloudlet load efficiently. Dynamic resource scaling can be done either horizontally or vertically. In horizontal dynamic resource scaling, the Host machines are equipped with the more number of resources rather than increasing the number of Host machines. In cloud computing, the service provider’s intention is to provide a cost-effective and efficient service support to its clients. The resources and services in cloud computing are on the pay-per-use basis, and hence, the horizontal dynamic resource scaling provides a good cost-effective solution for the resource requirement.
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References
Zhang S, Qian ZZ, Wu J et al (2015) Service-oriented resource allocation in clouds: pursuing flexibility and efficiency. J Comput Sci Technol 30(2):421–436
Anastasi GF, Coppola M, Dazzi P, Distefano M (2016) QoS Guarantees for network bandwidth in private clouds. In: CLOUD FORWARD: from distributed to complete computing, CF2016, 18–20 October 2016. Madrid, Spain, Proc Comput Sci 97:4–13
Barba-Jimenez C, Ramirez-Velarde R, Tchernykh A, Rodríguez-Dagnino R, Nolazco-Flores J, Perez-Cazares R (2016) Cloud based video-on-demand service model ensuring quality of service and scalability. J Netw Comput Appl
Sareen P, Kumar P, Singh TP Resource allocation strategies in cloud computing. Int J Comput Sci Commun Netw 5(6):358–365
Rajasekar B, Manigandan SK (2015) An efficient resource allocation strategies in cloud computing. Int J Innov Res Comput Commun Eng 3(2):1239–1244
Alnori A, Djemame K (2018) A holistic resource management for graphics processing units in cloud computing. Electron Notes Theoret Comput Sci 340:3–22
Gmach D, Rolia J, Cherkasova L, Kemper A (2007) Capacity management and demand prediction for next generation data Centers. In: Published in the international conference on web services (ICWS ‘2007), 9–13 July 2007. Salt Lake City, Utah
Lim H, Babu S, Chase J Automated control for elastic storage. In: Proceedings ICAC ’10, June 7–11, 2010. Washington, DC
Urgaonkar B, Pacifici MSG, Shenoy PJ, Tantawi AN (2005) An analytical model for multi-tier internet services and its applications. In: Proceedings SIGMETRICS, June 6–10, 2005. Banff, Alberta, Canada, ACM 1-59593-022-1
Toosi AN, Son J, Chi Q, Buyya R (2019) ElasticSFC: auto-scaling techniques for elastic service function chaining in network functions virtualization-based clouds. J Syst Softw
Baruah P, Mohanpurkar A (2015) Impact of elasticity on cloud systems. Int J Comput Appl (0975–8887) 120(14):23–28
Ghose S (2016) Testing elasticity of cloud platform. Int J Comput Eng Appl X(VI):58–66
Rehman Z, Hussain OK, Hussain FK, Chang E, Dillon T (2015) User-side QoS forecasting and management of cloud services. Received: 26 August 2014/Revised: 26 November 2014, Springer Science Business Media, New York
Singh J, Agarwal S, Mishra J (2015) A review: towards quality of service in cloud computing. Int J Sci Res (IJSR), 555–561. ISSN (Online): 2319-7064 Index Copernicus Value, 78.96|Impact Factor 6.391
Ghahramani MH, Zhou MC, Hon CT (2017) Toward cloud computing QoS architecture: analysis of cloud systems and cloud services. IEEE/CAA J Automatica Sinica 4(1):6–18
Rahamath Nazneen S, Kavitha R (2014) Cloud computing integrated with testing to ensure quality. Int J Adv Res Comput Sci Technol (IJARCST 2014), IJARCST 2(1):196–199. ISSN: 2347-8446 (Online), ISSN: 2347-9817 (Print)
Kivity A, Kamay Y, Laor D, Lublin U, Liguori A (2007) kvm: the Linux virtual machine monitor. In: Proceedings of the Ottawa Linux symposium, vol 1, pp 225–230
Choi J, Ahn Y, Kim S, Kim Y, Choi J (2015) VM auto-scaling methods for high throughput computing on hybrid infrastructure. Clust Comput 18:1063–1073
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Chaturvedi, A.K., Sengar, P., Sharma, K. (2021). Horizontal Dynamic Resource Scaling by Measuring the Impacts of Scheduling Interval in Cloud Computing. In: Goyal, V., Gupta, M., Trivedi, A., Kolhe, M.L. (eds) Proceedings of International Conference on Communication and Artificial Intelligence. Lecture Notes in Networks and Systems, vol 192. Springer, Singapore. https://doi.org/10.1007/978-981-33-6546-9_50
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DOI: https://doi.org/10.1007/978-981-33-6546-9_50
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