Abstract
Cloud computing is an emerging distributed computing paradigm that has become one of the extremely popular computing paradigms nowadays. One of the reasons for the popularity of cloud computing is due to its elasticity feature. Elasticity is a unique feature that enables the cloud platforms to add and remove resources “on the fly” to handle changes in workload demands. On the other hand, if the elasticity feature is not correctly managed, the cloud platforms may face over-provisioning or under-provisioning problems due to the arrival rate of users to the cloud applications varies over the time. Therefore, it necessitates the resource elasticity management issue as one of the challenging problems to be taken into account in the cloud computing environment. In this paper, we propose an elastic controller based on Colored Petri Nets to manage cloud infrastructures automatically. Finally, we evaluate the efficiency of the proposed elastic controller under three real workloads. The simulation results indicate that the proposed elastic controller reduces the response time by up to 4.8%, and increases the resource utilization and the elasticity by up to 9.3% and 6.7% respectively, compared with other approaches.
Similar content being viewed by others
References
Chandrasekaran, K.: Essentials of Cloud Computing. CRC Press, Boca Raton (2014)
Ghobaei-Arani, M., Souri, A., Baker, T., Hussien, A.: ControCity: an autonomous approach for controlling elasticity using buffer management in cloud computing environment. IEEE Access. 7, 106912–106924 (2019). https://doi.org/10.1109/ACCESS.2019.2932462
Herbst, N.R., Kounev, S., Reussner, R.H.: Elasticity in cloud computing: what it is, and what it is not. In: ICAC, vol. 13, pp. 23–27 (2013)
Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., Merle, P.: Elasticity in cloud computing: state of the art and research challenges. IEEE Trans. Serv. Comput. 11(2), 430–447 (2018)
Beltrán, M.: BECloud: a new approach to analyse elasticity enablers of cloud services. Future Gener. Comput. Syst. 64, 39–49 (2016)
Li, K.: Quantitative modeling and analytical calculation of elasticity in cloud computing. IEEE Trans. Cloud Comput. 4, 1–14 (2017)
Xu, C.Z., Rao, J., Bu, X.: URL: a unified reinforcement learning approach for autonomic cloud management. J. Parallel Distrib. Comput. 72(2), 95–105 (2012)
Hiba, S.H., Belguidoum, M.: Toward a meta-model for elasticity management in cloud applications. In: 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), pp. 1–6. IEEE (2017)
Salah, K., Elbadawi, K., Boutaba, R.: An analytical model for estimating cloud resources of elastic services. J. Netw. Syst. Manag. 24(2), 285–308 (2016)
Mohamed, M., Amziani, M., Belaïd, D., Tata, S., Melliti, T.: An autonomic approach to manage elasticity of business processes in the cloud. Future Gener. Comput. Syst. 50, 49–61 (2015)
Goswami, B., Sarkar, J., Saha, S., Kar, S., Sarkar, P.: ALVEC: auto-scaling by Lotka Volterra Elastic Cloud: a QoS aware non linear dynamical allocation model. arXiv preprint arXiv:1805.07356 (2018)
Kaur, P.D., Chana, I.: A resource elasticity framework for QoS-aware execution of cloud applications. Future Gener. Comput. Syst. 37, 14–25 (2014)
Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., Merle, P.: Autonomic vertical elasticity of docker containers with elasticdocker. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 472–479. IEEE (2017)
Garcia, A., Laneve, C., Lienhardt, M.: Static analysis of cloud elasticity. Sci. Comput. Program. 147, 27–53 (2017)
Coutinho, E.F., Rego, P.A., Gomes, D.G., de Souza, J.N.: Physics and microeconomics-based metrics for evaluating cloud computing elasticity. J. Netw. Comput. Appl. 63, 159–172 (2016)
Hwang, K., Bai, X., Shi, Y., Li, M., Chen, W.G., Wu, Y.: Cloud performance modeling with benchmark evaluation of elastic scaling strategies. IEEE Trans. Parallel Distrib. Syst. 27(1), 130–143 (2016)
Galante, G., De Bona, L.C.E.: A programming-level approach for elasticizing parallel scientific applications. J. Syst. Softw. 110, 239–252 (2015)
Fe, I., Matos, R., Dantas, J., Melo, C., Maciel, P.: Stochastic model of performance and cost for auto-scaling planning in public cloud. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2081–2086. IEEE (2017)
Gehlot, V., Nigro, C.: An introduction to systems modeling and simulation with Colored Petri Nets. In: Proceedings of the Winter Simulation Conference, pp. 104–118. Winter Simulation Conference (2010)
Jensen, K.: Coloured Petri Nets: Basic Concepts, Analysis Methods and Practical Use, vol. 1. Springer, Berlin (2013)
Mortensen, K.H.: Coloured Petri Nets-a Pragmatic Formal Method for Designing and Analysing Distributed Systems. DAIMI Report Series, vol. 26(522) (1997)
Badger, L., Grance, T., Patt-Corner, R., Voas, J.: Draft cloud computing synopsis and recommendations. NIST Spec. Publ. 800, 146 (2011)
Bikas, M.A.N., Alourani, A., Grechanik, M.: How elasticity property plays an important role in the cloud: a survey. In: Advances in Computers, vol. 103, pp. 1–30. Elsevier, Amsterdam (2016)
Galante, G., Bona, L.C.E.D.: A survey on cloud computing elasticity. In: Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing, pp. 263–270. IEEE Computer Society (2012)
Amazon: http://aws.amazon.com
Rightscale: http://www.rightscale.com
Ai, W., Li, K., Lan, S., Zhang, F., Mei, J., Li, K., Buyya, R.: On elasticity measurement in cloud computing. In: Scientific Programming, vol. 2016 (2016)
Westergaard, M.: CPN Tools 4: multi-formalism and extensibility. In: International Conference on Applications and Theory of Petri Nets and Concurrency, pp. 400–409. Springer, Berlin (2013)
Calheiros, R.N., Ranjan, R., Beloglazov, A., DeRose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw.: Pract. Exp. 41(1), 23–50 (2011)
Gallasch, G.E., Kristensen, L.M.: Comms/CPN: A communication infrastructure for external communication with design/CPN (Doctoral dissertation, Aarhus University) (2001)
Reiss, C., Wilkes, J., Hellerstein, J.L.: Google cluster-usage traces: format + schema. Google Inc., White Paper, pp. 1–14 (2011)
Urdaneta, G., Pierre, G., Van Steen, M.: Wikipedia workload analysis for decentralized hosting. Comput. Netw. 53(11), 1830–1845 (2009)
Aslanpour, M.S., Dashti, S.E., Ghobaei-Arani, M., Rahmanian, A.A.: Resource provisioning for cloud applications: a 3-D, provident and flexible approach. J. Supercomput. 74(12), 6470–6501 (2018)
Aslanpour, M.S., Dashti, S.E.: Proactive auto-scaling algorithm (PASA) for cloud application. Int. J. Grid High Perform. Comput. 9(3), 1–16 (2017)
Acknowledgements
The authors would like to thank the Islamic Azad University of Qom Branch for supporting this paper under the research project titled “Quantitative Modeling of Elasticity Feature in the Cloud Environment”, records with grant No.97-2-265867.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shahidinejad, A., Ghobaei-Arani, M. & Esmaeili, L. An elastic controller using Colored Petri Nets in cloud computing environment. Cluster Comput 23, 1045–1071 (2020). https://doi.org/10.1007/s10586-019-02972-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-019-02972-8