Abstract
The cloud resource provisioning is a mechanism of cloud resources allocation to cloud customers, and cloud customers have to interact with cloud resources using any cloud data center. The workload of the cloud environment consists of the significance of computing resources running situation in the cloud data centers. Cloud resource provisioning has a signification relation with cloud workload. The workload of cloud data centers is not the same all the time. For smooth and effective working of cloud resources at the cloud customer end, scaling of cloud resources required at cloud data center end. The scaling is a primary plan that to manage the extended work-load of the cloud data center. Scaling is implemented by adding additional or increasing computing power and memory capacity. Auto-scaling is one of an essential attribute of cloud computing that facilitates automatic provisioning of computing resources like add, remove, scale-up or scale-down resources depending upon workload. Big data applications associated with the large storage capacity and high processing power, cloud environment is suitable for fulfilling big data application requirement using auto-scaling of resources. In the present study, we have estimated the survival probability of auto-scaled cloud environment in the context of big data applications. Further, we investigated in this paper the importance of cloud resources that are used to build auto-scaling based cloud computing environment.
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Sosinsky, B.: Cloud Computing Bible. Wiley Publishing Inc., Indianapolis (2011)
Bills, D.: Fundamentals of Cloud Service Reliability. https://cloudblogs.microsoft.com/microsoftsecure/2014/03/24/reliability-series-1-reliability-vs-resilience/. Accessed 29 Nov 2018
Kaur, G., Kumar, R.: A review on reliability issues in cloud service. In: Proceeding of International Conference on Advancements in Engineering and Technology (ICAET 2015), pp. 9–13 (2015). Int. J. Comput. Appl.
Dui, H.: Reliability optimization of automatic control systems based on importance measures: a framework. Int. J. Performability Eng. 12(3), 297–300 (2016)
Abawajy, J.: What is workload (cloud data center service provisioning: theoretical and practical approaches). https://www.jnu.ac.in/content/LAB05/presentation/gian2018/day2.pdf. Accessed 9 Sept 2018
Rausand, M., Hayland, A.: System Reliability Theory Models, Statistical Methods, and Applications, 2nd edn. Wiley, Hoboken (2004)
Adams, M.: An Introduction to designing reliable Cloud Services. https://chapters.cloudsecurityalliance.org/seattle/files/2013/08/An-Introduction. Accessed 8 Aug 2018
Sah, N., Singh, S.B., Rajput, R.S.: Stochastic analysis of a Web Server with different types of failure. J. Reliab. Stat. Stud. 3(1), 105–111 (2011)
Yadav, N., Singh, V.B., Kumari, M.: Generalized reliability model for cloud computing. Int. J. Comput. Appl. 88(14), 13–16 (2014)
Nabeela, N.: All you need to know about cloud computing. http://eid100nujhatn.blogspot.in/2015/10/all-you-need-to-know-about-cloud.html. Accessed 20 Oct 2018
Rajput, R.S., Pant, A.: Optimal resource management in the cloud environment - a review. Int. J. Converging Technol. Manag. (IJCTM) 4(1), 12–24 (2018)
Rajput, R.S., Goyal, D., Singh, S.B.: Study of performance evolution of three-tier architecture based cloud computing system. In: Proceeding of Third International Conference on Internet of Things and Connected Technologies (ICIoTCT) (2018). http://dx.doi.org/10.2139/ssrn.3166719
Rajput, R.S., Goyal, D., Pant, A.: The survival analysis of three-tier architecture based cloud computing system. Int. J. Adv. Stud. Sci. Res. 3(11), 300–305 (2018). http://ssrn.com/abstract=3320440
RightScale Docs: Cloud Computing System Architecture Diagrams. http://docs.rightscale.com/cm/designers_guide/cm-cloud-computing-system-architecture-diagram.html. Accessed 20 Oct 2018
Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: Auto-scaling techniques for elastic applications in cloud environments. Technical report, Department of Computer Architecture and Technology University of the Basque Country (2012)
Arora, Y., Goyal, D.: Big data technologies: brief overview. Int. J. Comput. Appl. 131(9), 1–6 (2015)
Agarwal, B., Ramampiaro, H., Langseth, H., Ruocco, M.: A deep network model for paraphrase detection in short text messages. Inf. Process. Manag. 54(6), 922–937 (2018)
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Rajput, R.S., Goyal, D., Pant, A. (2019). The Survival Analysis of Big Data Application Over Auto-scaling Cloud Environment. In: Somani, A., Ramakrishna, S., Chaudhary, A., Choudhary, C., Agarwal, B. (eds) Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics. ICETCE 2019. Communications in Computer and Information Science, vol 985. Springer, Singapore. https://doi.org/10.1007/978-981-13-8300-7_13
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DOI: https://doi.org/10.1007/978-981-13-8300-7_13
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