A Hybrid Approach for Improving Elasticity in the Cloud Computing Environment

Document Type : Original Article

Author

Faculty of Engineering, Qom Branch, Islamic Azad University, Qom, Iran

Abstract

Elasticity is considered as one of the most important features that distinguishes cloud computing from other distributed system approaches. This feature takes into account the fact that the resource allocation process is considered as a process that can be implemented dynamically. Providing an efficient solution for improving elasticity will be useful for both providers and users of cloud computing services. Using the proposed solution in this paper, providers will be able to evaluate and improve the quality of their services, and increase their qualitative or quantitative advantage in competing with other competitors. In this paper, we present a hybrid solution for improving elasticity through using buffer management and centralized elastic management. Buffer managment controls the input queue of the request and the elastic management by using the reinforcement learning controls the elasticity of the system. Finally, we evaluate the effectiveness of our approach under three real workload traces, namely, Yahoo Cluster, Wikipedia, and Google Cluster workload traces. The experimental results show that the proposed approach reduces the response time by up to 15.2%, and increases the resource utilization by up to 13.2 % and the elasticity by up to 19.8 % compared with the CTMC and ControCity approaches.

Keywords


[1] Zhang, Qi, Lu Cheng, and Raouf Boutaba. "Cloud computing: state-of-the-art and research challenges." Journal of internet services and applications vol.1, no. 1,pp.7-18, 2010.
[2] شهرام جمالی و سمیرا حورعلی،» موازنه گر نامتمرکز بار در محیط ابر با بهره گیری از سیاست تصمیم‌گیری چند شاخصه»، مجله مهندسی برق دانشگاه تبریز، جلد 46، شماره 3، صفحه 106-96، 1395.
[3] Ghobaei-Arani, Mostafa, Sam Jabbehdari, and Mohammad Ali Pourmina. "An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach." Future Generation Computer Systems vol. 78, no. 1, pp.191-210, 2018.
[4] سیمین قاسمی فلاورجانی، محمدعلی نعمت بخش و بهروز شاهقلی قهفرخی،» تخصیص وظایف چند هدفه در واگذاری به ابر سیار»، مجله مهندسی برق دانشگاه تبریز، جلد 46، شماره 4، صفحه 232-217، 1395.
[5] Herbst, Nikolas Roman, Samuel Kounev, and Ralf Reussner. "Elasticity in cloud computing: What it is, and what it is not." In Proceedings of the 10th International Conference on Autonomic Computing ({ICAC} 13), pp. 23-27. 2013.
[6] Al-Dhuraibi, Yahya, Fawaz Paraiso, Nabil Djarallah, and Philippe Merle. "Elasticity in cloud computing: state of the art and research challenges." IEEE Transactions on Services Computing vol 11, no.1, pp.430-447, 2017.
[7] P. D. Kaur and I. Chana, "A resource elasticity framework for QoS-aware execution of cloud applications," Future Generation Computer Systems, vol. 37, pp. 14-25, 2014.
[8] Botvinick, Mathew, et al. "Reinforcement learning, fast and slow." Trends in cognitive sciences vol.23, no. 5, pp.408-422, 2019.
[9] Beltrán, Marta. "BECloud: A new approach to analyse elasticity enablers of cloud services." Future Generation Computer Systems vol.64, no.1, pp.39-49, 2016.
[10] Li, Keqin. "Quantitative modeling and analytical calculation of elasticity in cloud computing." IEEE Transactions on Cloud Computing, 2017.
[11] Ghobaei-Arani, M., Souri, A., Baker, T. and Hussien, A., "ControCity: An Autonomous Approach for Controlling Elasticity Using Buffer Management in Cloud Computing Environment." IEEE Access vol. 7, no. 1, pp.106912-106924, 2019.
[12] Ullah, Amjad, Jingpeng Li, Yindong Shen, and Amir Hussain. "A control theoretical view of cloud elasticity: taxonomy, survey and challenges." Cluster Computing vol 21, no. 4, pp.1735-1764, 2018.
[13] Han, Rui. "Investigations into elasticity in cloud computing." arXiv preprint arXiv:1511.04651, 2015.
[14] Albonico, Michel, Jean-Marie Mottu, and Gerson Sunyé. "Controlling the elasticity of web applications on cloud computing." In Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 816-819. ACM, 2016.
[15] Computing, Autonomic. "An architectural blueprint for autonomic computing." IBM White Paper vol.31, no.1, pp. 1-6, 2006.
[16] Huebscher, Markus C., and Julie A. McCann. "A survey of autonomic computing—degrees, models, and applications." ACM Computing Surveys (CSUR) vol. 40, no. 3, p.7, 2008.
[17] Hariri, Salim, Bithika Khargharia, Houping Chen, Jingmei Yang, Yeliang Zhang, Manish Parashar, and Hua Liu. "The autonomic computing paradigm." Cluster Computing vol.9, no. 1, pp.5-17, 2006.
[18] https://docs.aws.amazon.com/batch/latest/userguide/job_scheduling.html
[19] Messias, Valter Rogério, Julio Cezar Estrella, Ricardo Ehlers, Marcos José Santana, Regina Carlucci Santana, and Stephan Reiff-Marganiec. "Combining time series prediction models using genetic algorithm to autoscaling web applications hosted in the cloud infrastructure." Neural Computing and Applications vol. 27, no. 8, pp. 2383-2406, 2016.
[20] Calheiros, Rodrigo N., Rajiv Ranjan, Anton Beloglazov, César AF De Rose, and Rajkumar Buyya. "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms." Software: Practice and experience vol. 41, no. 1, pp. 23-50, 2011.
[21] https://webscope.sandbox.yahoo.com/catalog.php?datatype=s
[22] Reiss, C., Wilkes, J., Hellerstein, J.L.: Google cluster-usage traces: format ? schema. Google Inc., White Paper, pp. 1–14, 2011
[23] Urdaneta, G., Pierre, G., Van Steen, M.:"Wikipedia workload analysis for decentralized hosting." Comput. Netw. vol. 53, no. 11, pp.1830–1845, 2009.