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Dynamic Scalability Model for Containerized Cloud Services

  • Research Article - Computer Engineering and Computer Science
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Abstract

Cloud computing has become an important research area in large-scale computing systems and is being employed by many organizations in government, businesses, and industry. Schemes and appropriate models for dynamic resources provisioning in the cloud environment have been extensively studied. To date, the research literature is lacking schemes and models that offer dynamic scalability in which Quality of Service (QoS) and high performance are provided to customers with the usage of the least number of cloud resources, especially for containerized services hosted on the cloud. With dynamic scalability, cloud computing can offer on-demand, timely, and dynamically adjustable computing resources to services hosted on the cloud. This paper presents a dynamic scaling model based on queueing theory to scale containers virtual resources and satisfy the customer Service Level Agreements (SLA) while guarding costs of scaling very low. The aim is to improve the virtual computing resources utilization and satisfy SLA constraints in terms of CPU utilization, system response time, system drop rate, system number of tasks, and system throughput. Simulation results are provided using Java Modelling Tools simulation tool, which shows that our proposed model can determine under any offered workload the needed containers instances to satisfy the required QoS parameters.

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References

  1. Buyya, R.; Yeo, C.S.; Venugopal, S.; Broberg, J.; Brandic, I.: Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Computer Syst. 25(6), 599–616 (2009)

    Article  Google Scholar 

  2. Zhu, X.; Chen, C.; Yang, L.T.; Xiang, Y.: ANGEL: agent-based scheduling for real-time tasks in virtualized clouds. IEEE Trans. Comput. 64(12), 3389–3403 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  3. Eramo, V.; Lavacca, F.G.: Optimizing the cloud resources, bandwidth and deployment costs in multi-providers network function virtualization environment. IEEE Access 7, 46898–46916 (2019)

    Article  Google Scholar 

  4. Rosenblum, M.: The reincarnation of virtual machines. Queue 2(5), 34 (2004)

    Article  Google Scholar 

  5. Varghese, B.; Buyya, R.: Next generation cloud computing: new trends and research directions. Future Gener. Computer Syst. 79, 849–861 (2018)

    Article  Google Scholar 

  6. El Kafhali, S.; Salah, K.: Modeling and analysis of performance and energy consumption in cloud data centers. Arab. J. Sci. Eng. 43(12), 7789–7802 (2018)

    Article  Google Scholar 

  7. El Kafhali, S.; Salah, K.: Stochastic modelling and analysis of cloud computing data center. In: Proceedings of the 20th Conference on Innovations in Clouds, Internet and Networks (ICIN’17), IEEE, pp. 122–126 (2017).

  8. Taherizadeh, S.; Stankovski, V.: Dynamic multi-level auto-scaling rules for containerized applications. Comput. J. 62(2), 174–197 (2018)

    Article  Google Scholar 

  9. Khazaei, H.; Barna, C.; Beigi-Mohammadi, N.; Litoiu, M.: Efficiency analysis of provisioning microservices. In: Proceedings of the International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, pp. 261–268 (2016).

  10. Jamshidi, P.; Pahl, C.; Mendonça, N.C.; Lewis, J.; Tilkov, S.: Microservices: the journey so far and challenges ahead. IEEE Softw. 35(3), 24–35 (2018)

    Article  Google Scholar 

  11. Bernstein, D.: Containers and cloud: from lxc to docker to Kubernetes. IEEE Cloud Comput. 1(3), 81–84 (2014)

    Article  Google Scholar 

  12. Saadi, Y.; El Kafhali, S.: Energy-efficient strategy for virtual machine consolidation in cloud environment. Soft. Comput. (2020). https://doi.org/10.1007/s00500-020-04839-2

    Article  Google Scholar 

  13. Pahl, C.: Containerization and the PaaS cloud. IEEE Cloud Comput. 2(3), 24–31 (2015)

    Article  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. Al-Sharif, Z.A.; Jararweh, Y.; Al-Dahoud, A.; Alawneh, L.M.: ACCRS: autonomic based cloud computing resource scaling. Clust. Comput. 20(3), 2479–2488 (2016)

    Article  Google Scholar 

  16. Islam, S.; Keung, J.; Lee, K.; Liu, A.: Empirical prediction models for adaptive resource provisioning in the cloud. Future Gener. Computer Syst. 28(1), 155–162 (2012)

    Article  Google Scholar 

  17. Salah, K.; Elbadawi, K.; Boutaba, R.: An analytical model for estimating cloud resources of elastic services. J. Netw. Syst. Manage. 24(2), 285–308 (2016)

    Article  Google Scholar 

  18. Martin, J.P.; Kandasamy, A.; Chandrasekaran, K.: Exploring the support for high performance applications in the container runtime environment. Human-Centric Comput. Inf. Sci. 8(1), 1–15 (2018)

    Article  Google Scholar 

  19. Soltesz, S.; Pötzl, H.; Fiuczynski, M.E.; Bavier, A.; Peterson, L.: Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors. ACM SIGOPS Oper. Syst. Rev. 41(3), 275–287 (2007)

    Article  Google Scholar 

  20. Merkel, D.: Docker: lightweight linux containers for consistent development and deployment. Linux J. 2014(239), 2 (2014)

    Google Scholar 

  21. Rizki, R.; Rakhmatsyah, A.; Nugroho, M. A.: Performance analysis of container-based Hadoop cluster: openVZ and LXC. In: Proceedings of the 4th International Conference on Information and Communication Technology (ICoICT), IEEE, pp. 1–4 (2016).

  22. Memari, N.; Hashim, S. J. B.; Samsudin, K. B.: Towards virtual honeynet based on LXC virtualization. In: Proceedings of the Region 10 Symposium, IEEE, pp. 496–501 (2014).

  23. Aguilera, X. M.; Otero, C.; Ridley, M.; Elliott, D.: Managed containers: a framework for resilient containerized mission critical systems. In: Proceedings of the 11th International Conference on Cloud Computing (CLOUD), IEEE, pp. 946–949 (2018).

  24. Cai, L.; Qi, Y.; Wei, W.; Li, J.: Improving resource usages of containers through auto-tuning container resource parameters. IEEE Access 7, 108530–108541 (2019)

    Article  Google Scholar 

  25. Yadav, A. K.; Garg, M. L.: Docker containers versus virtual machine-based virtualization. In: Proceedings of the 1st International Conference on Emerging Technologies in Data Mining and Information Security, Springer, pp. 141–150 (2019).

  26. Xavier, M. G.; De Oliveira, I. C.; Rossi, F. D.; Dos Passos, R. D.; Matteussi, K. J.; De Rose, C. A.: A performance isolation analysis of disk-intensive workloads on container-based clouds. In: Proceedings of the 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, IEEE, pp. 253–260 (2015).

  27. Liu, B.; Chen, Y.: A scalable fine-grained analytic model for container cloud data centres. Int. J. Internet Technol. Secur. Trans. 9(4), 355–389 (2019)

    Article  Google Scholar 

  28. Podolskiy, V.; Jindal, A.; Gerndt, M.: Multilayered autoscaling performance evaluation: can virtual machines and containers co–scale? Int. J. Appl. Math. Comput. Sci. 29(2), 227–244 (2019)

    Article  Google Scholar 

  29. Ye, T.; Guangtao, X.; Shiyou, Q.; Minglu, L.: An auto-scaling framework for containerized elastic applications. In: Proceedings of the 3rd International Conference on Big Data Computing and Communications (BIGCOM), IEEE, pp. 422–430 (2017).

  30. Moreno-Vozmediano, R.; Montero, R.S.; Huedo, E.; Llorente, I.M.: Efficient resource provisioning for elastic Cloud services based on machine learning techniques. J. Cloud Comput. 8(1), 1–18 (2019)

    Article  Google Scholar 

  31. Al-Dhuraibi, Y.; Paraiso, F.; Djarallah, N.; Merle, P.: Autonomic vertical elasticity of Docker containers with ELASTICDOCKER. In: Proceedings of the 10th International Conference on Cloud Computing (CLOUD), IEEE, pp. 472–479 (2017).

  32. Abbasipour, M.; Khendek, F.; Toeroe, M.: A model-based approach for design time elasticity rules generation. In: Proceedings of the 23rd International Conference on Engineering of Complex Computer Systems, IEEE, pp. 93–103 (2018).

  33. Yong, C.; Lee, G.W.; Huh, E.N.: Proposal of container-based HPC structures and performance analysis. J. Inf. Process. Syst. 14(6), 1398–1404 (2018)

    Google Scholar 

  34. Shen, Z.; Sun, Z.; Sela, G. E.; Bagdasaryan, E.; Delimitrou, C.; Van Renesse, R.; Weatherspoon, H.: X-containers: breaking down barriers to improve performance and isolation of cloud-native containers. In: Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, ACM, pp. 121–135 (2019).

  35. El Kafhali, S.; Salah, K.: Efficient and dynamic scaling of fog nodes for IoT devices. J. Supercomput. 73(12), 5261–5284 (2017)

    Article  Google Scholar 

  36. Sun, Y.; Meng, L.; Song, Y.: AutoScale: adaptive QoS-Aware container-based cloud applications scheduling framework. KSII Trans. Internet Inf. Syst. (TIIS) 13(6), 2824–2837 (2019)

    Google Scholar 

  37. Hanini, M.; El Kafhali, S.; Salah, K.: Dynamic VM allocation and traffic control to manage QoS and energy consumption in cloud computing environment. Int. J. Comput. Appl. Technol. 60(4), 307–316 (2019)

    Article  Google Scholar 

  38. Rista, C.; Teixeira, M.; Griebler, D.; Fernandes, L. G.: Evaluating, estimating, and improving network performance in container-based clouds. In: Proceedings of the Symposium on Computers and Communications (ISCC), IEEE, pp. 514–520 (2018).

  39. Jia, R.; Yang, Y.; Grundy, J.; Keung, J.; Li, H. A deadline constrained preemptive scheduler using queuing systems for multi-tenancy clouds. In: Proceedings of the 12th International Conference on Cloud Computing (CLOUD), IEEE, pp. 63–67 (2019).

  40. Kabashkin, I.: Availability of applications in container-based cloud PaaS architecture. In: Proceedings of the International Conference on Reliability and Statistics in Transportation and Communication. Springer, Cham, pp. 241–248 (2018).

  41. El Kafhali, S.; Salah, K.: Performance modeling and analysis of internet of things enabled healthcare monitoring systems. IET Netw. 8(1), 48–58 (2019)

    Article  Google Scholar 

  42. Chen, H.; Yao, D.D.: Fundamentals of Queueing Networks: Performance, Asymptotics, and Optimization, vol. 46. Springer, Berlin (2013)

    MATH  Google Scholar 

  43. Nelson, R.: Probability, Stochastic Processes, and Queueing Theory: the Mathematics of Computer Performance Modeling. Springer, Berlin (2013)

    Google Scholar 

  44. Salah, K.; El Kafhali, S.: Performance modeling and analysis of hypoexponential network servers. J. Telecommun. Syst. 65(4), 717–728 (2017)

    Article  Google Scholar 

  45. Burke, P.J.: The output of a queuing system. Oper. Res. 4(6), 699–704 (1956)

    Article  MathSciNet  MATH  Google Scholar 

  46. El Kafhal, S.; Salah, K.; Ben Alla, S.: Performance evaluation of IoT-fog-cloud deployment for healthcare services. In: Proceedings of the Fourth International Conference on Cloud Computing Technologies and Applications (CloudTech’18), IEEE, pp. 1–6 (2018).

  47. Bhat, U.N.: An Introduction to Queueing Theory: Modeling and Analysis in Applications. Springer, New York (2015)

    Book  MATH  Google Scholar 

  48. El Kafhali, S.; Salah, K.: Performance analysis of multi-core VMs hosting cloud SaaS applications. Computer Standards Interfaces 55, 126–135 (2018)

    Article  Google Scholar 

  49. Bertoli, M.; Casale, G.; Serazzi, G.: JMT: performance engineering tools for system modeling. ACM SIGMETRICS Perform. Eval. Rev. 36(4), 10–15 (2009)

    Article  Google Scholar 

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Acknowledgements

The authors thank the anonymous reviewers for their valuable comments, which helped us to considerably improve the content, quality, and presentation of this paper.

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Authors and Affiliations

  1. Computer, Networks, Mobility and Modeling Laboratory: IR2M, Faculty of Sciences and Techniques, Hassan First University of Settat, 26000, Settat, Morocco

    Said El Kafhali & Mohamed Hanini

  2. Advanced Science and Technologies Laboratory, Computer Sciences Department, Polydisciplinary Faculty, Abdelmalek Essaadi University, Larache, Morocco

    Iman El Mir

  3. Electrical and Computer Engineering Department, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

    Khaled Salah

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  1. Said El Kafhali
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  2. Iman El Mir
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  3. Khaled Salah
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  4. Mohamed Hanini
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Correspondence to Said El Kafhali.

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El Kafhali, S., El Mir, I., Salah, K. et al. Dynamic Scalability Model for Containerized Cloud Services. Arab J Sci Eng 45, 10693–10708 (2020). https://doi.org/10.1007/s13369-020-04847-2

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