Elasticity Management in Cloud Computing Using Colored Petri Net

Document Type : Original Article

Author

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

Abstract

Cloud computing is a new technology which its popularity increases every day, a popularity due to its elasticity. On the other words, cloud computing takes into account an unlimited capacity of the resource for the consumer, and the consumer can take resources in demand based on competitive rates and increase or decrease the amount of resources. There have been many improvements to elasticity management by previous researches. However, further reasearches are necessary to manage elasticity more efficiently. In this paper, a model for the elasticity improvement using a colored Petri network is proposed to provide resources in cloud computing. In the proposed model, elasticity management is performed using a colored Petri net in the form of control of the M/M/N queues. In this way, there is a horizontal queue for each request or service in the vertical queue for the need to increase or decrease the virtual machine. The results of the proposed method show an improvment in elasticity, accuracy and speed, compared with the other approaches.

Keywords


[1]        شهرام جمالی، سمیرا حورعلی، «موازنه‌گر نامتمرکز بار در محیط ابر با بهره‌گیری از سیاست تصمیم‌گیری چندشاخصه»، مجله مهندسی برق دانشگاه تبریز، جلد 46، شماره 3، صفحات 106-95، 1395.
[2]           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.
[3]           F. Paraiso, P. Merle, and L. Seinturier, "soCloud: a service-oriented component-based PaaS for managing portability, provisioning, elasticity, and high availability across multiple clouds," Computing, vol. 98, pp. 539-565, 2016.
[4]           E. Barrett, E. Howley, and J. Duggan, "Applying reinforcement learning towards automating resource allocation and application scalability in the cloud," Concurrency and Computation: Practice and Experience, vol. 25, pp. 1656-1674, 2013.
[5]           Y. Tan, H. Nguyen, Z. Shen, X. Gu, C. Venkatramani, and D. Rajan, "Prepare: Predictive performance anomaly prevention for virtualized cloud systems," in Distributed Computing Systems (ICDCS), 2012 IEEE 32nd International Conference on, 2012, pp. 285-294.
[6]           L. R. Moore, K. Bean, and T. Ellahi, "Transforming reactive auto-scaling into proactive auto-scaling," in Proceedings of the 3rd International Workshop on Cloud Data and Platforms, 2013, pp. 7-12.
[7]           G. A. Moreno, J. Cámara, D. Garlan, and B. Schmerl, "Efficient decision-making under uncertainty for proactive self-adaptation," in Autonomic Computing (ICAC), 2016 IEEE International Conference on, 2016, pp. 147-156.
[8]           G. A. Moreno, J. Cámara, D. Garlan, and B. Schmerl, "Proactive self-adaptation under uncertainty: a probabilistic model checking approach," in Proceedings of the 2015 10th joint meeting on foundations of software engineering, 2015, pp. 1-12.
[9]           E. B. Lakew, C. Klein, F. Hernandez-Rodriguez, and E. Elmroth, "Towards faster response time models for vertical elasticity," in Utility and Cloud Computing (UCC), 2014 IEEE/ACM 7th International Conference on, 2014, pp. 560-565.
[10]         L. Baresi, S. Guinea, A. Leva, and G. Quattrocchi, "A discrete-time feedback controller for containerized cloud applications," in Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 2016, pp. 217-228.
[11]         S. Lehrig, R. Sanders, G. Brataas, M. Cecowski, S. Ivanšek, and J. Polutnik, "CloudStore—towards scalability, elasticity, and efficiency benchmarking and analysis in Cloud computing," Future Generation Computer Systems, vol. 78, pp. 115-126, 2018.
[12]         A. Ashraf, B. Byholm, and I. Porres, "CRAMP: Cost-efficient resource allocation for multiple web applications with proactive scaling," in Cloud Computing Technology and Science (CloudCom), 2012 IEEE 4th International Conference on, 2012, pp. 581-586.
[13]         A. Beloglazov and R. Buyya, "Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers," in MGC@ Middleware, 2010, p. 4.
[14]         K. Li, "Quantitative modeling and analytical calculation of elasticity in cloud computing," IEEE Transactions on Cloud Computing, 2017.
[15]         M. Beltrán, "BECloud: A new approach to analyse elasticity enablers of cloud services," Future Generation Computer Systems, vol. 64, pp. 39-49, 2016.
[16]         V. Cardellini, T. G. Grbac, M. Nardelli, N. Tanković, and H.-L. Truong, "QoS-Based Elasticity for Service Chains in Distributed Edge Cloud Environments," in Autonomous Control for a Reliable Internet of Services, ed: Springer, 2018, pp. 182-211.
[17]         S. M.-K. Gueye, N. De Palma, É. Rutten, A. Tchana, and N. Berthier, "Coordinating self-sizing and self-repair managers for multi-tier systems," Future Generation Computer Systems, vol. 35, pp. 14-26, 2014.
[18]         L. M. Vaquero, D. Morán, F. Galán, and J. M. Alcaraz-Calero, "Towards runtime reconfiguration of application control policies in the cloud," Journal of Network and Systems Management, vol. 20, pp. 489-512, 2012.
[19]         R. Han, M. M. Ghanem, L. Guo, Y. Guo, and M. Osmond, "Enabling cost-aware and adaptive elasticity of multi-tier cloud applications," Future Generation Computer Systems, vol. 32, pp. 82-98, 2014.
[20]         A. Al-Shishtawy and V. Vlassov, "Elastman: elasticity manager for elastic key-value stores in the cloud," in Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, 2013, p. 7.
[21]         D. Serrano, S. Bouchenak, Y. Kouki, T. Ledoux, J. Lejeune, J. Sopena, et al., "Towards qos-oriented sla guarantees for online cloud services," in Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on, 2013, pp. 50-57.
[22]         Y. Al-Dhuraibi, F. Zalila, N. Djarallah, and P. Merle, "Coordinating Vertical Elasticity of both Containers and Virtual Machines," in CLOSER 2018-8th International Conference on Cloud Computing and Services Science, 2018, pp. 1-8.
[23]         A. da Silva Dias, L. H. Nakamura, J. C. Estrella, R. H. Santana, and M. J. Santana, "Providing IaaS resources automatically through prediction and monitoring approaches," in Computers and Communication (ISCC), 2014 IEEE Symposium on, 2014, pp. 1-7.
[24]         E. Caron, F. Desprez, and A. Muresan, "Forecasting for Cloud computing on-demand resources based on pattern matching," INRIA, 2010.
[25]         T. Bhardwaj and S. C. Sharma, "Fuzzy logic-based elasticity controller for autonomic resource provisioning in parallel scientific applications: a cloud computing perspective," Computers & Electrical Engineering, 2018.
[26]         J. Huang, C. Li, and J. Yu, "Resource prediction based on double exponential smoothing in cloud computing," in Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on, 2012, pp. 2060-2056.
[27]         W. Iqbal, M. N. Dailey, D. Carrera, and P. Janecek, "Adaptive resource provisioning for read intensive multi-tier applications in the cloud," Future Generation Computer Systems, vol. 27, pp. 871-879, 2011.
[28]         C. Kan, "DoCloud: An elastic cloud platform for Web applications based on Docker," in Advanced Communication Technology (ICACT), 2016 18th International Conference on, 2016, pp. 478-483.
[29]         K. Salah, K. Elbadawi, and R. Boutaba, "An analytical model for estimating cloud resources of elastic services," Journal of Network and Systems Management, vol. 24, pp. 285-308, 2016.
[30]         C.-Z. Xu, J. Rao, and X. Bu, "URL: A unified reinforcement learning approach for autonomic cloud management," Journal of Parallel and Distributed Computing, vol. 72, pp. 95-105, 2012.
[31]         Y. Al-Dhuraibi, F. Paraiso, N. Djarallah, and P. Merle, "Elasticity in cloud computing: state of the art and research challenges," IEEE Transactions on Services Computing, vol. 11, pp. 430-447, 2018.
[32]         W. M. Van der Aalst, "The application of Petri nets to workflow management," Journal of circuits, systems, and computers, vol. 8, pp. 21-66, 1998.
[33]         K. Jensen and G. Rozenberg, High-level Petri nets: theory and application: Springer Science & Business Media, 2012.
[34]       سعید پاشازاده، «تحلیل خودکار بازی رایانه‌ای با استفاده از شبکه پتری رنگی»، مجله مهندسی برق دانشگاه تبریز، جلد 46، شماره 2، صفحات 48-37، 1395.
[35]         W. Ai, K. Li, S. Lan, F. Zhang, J. Mei, K. Li, et al., "On elasticity measurement in cloud computing," Scientific Programming, vol, 2016.
[36]         N. R. Herbst, S. Kounev, and R. H. Reussner, "Elasticity in Cloud Computing: What It Is, and What It Is Not," in ICAC, 2013, pp. 23-27.
[37]         C. Reiss, J. Wilkes, and J. L. Hellerstein, "Google cluster-usage traces: format+ schema," Google Inc., White Paper, pp. 1-14, 2011 .