Performance Evaluations of a Cloud Computing Physical Machine with Task Reneging and Task Resubmission (Feedback)

  • Godlove Suila Kuaban
  • Bhavneet Singh SoodanEmail author
  • Rakesh KumarEmail author
  • Piotr CzekalskiEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1231)


Cloud service providers (CSP) provide on-demand cloud computing services, reduces the cost of setting-up and scaling-up IT infrastructure and services, and stimulates shorter establishment times for start-ups that offer or use cloud-based services. Task reneging or dropping sometimes occur when a task waits in the queue longer than its timeout or execution deadline, or it is compromised and must be dropped from the queue or as an active queue management strategy to avoid tail dropping of tasks when the queues are full. Reneged or dropped tasks could be resubmitted provided they were not dropped due to security reasons. In this paper, we present a simple M/M/c/N queueing model of a cloud computing physical machine, where the interarrival times and the services times are exponentially distributed, with N buffer size and c virtual machines running in parallel. We present numerical examples to illustrate the effect of task reneging and task resubmission on the queueing delay, probability of task rejection, and the probability of immediate service.


Transient-state Steady-state Performance evaluations Cloud computing Physical machines Tasks reneging or dropping Tasks resubmission or feedback 


  1. 1.
    Buyya, R., et al.: A manifesto for future generation cloud computing: research directions for the next decade. ACM Comput. Surv. 51(5), 38 (2018). Article no. 105 CrossRefGoogle Scholar
  2. 2.
    Paya, A., Marinescu, D.C.: Energy-aware load balancing and application scaling for the cloud ecosystem. IEEE Trans. Cloud Comput 5(1), 15–27 (2017)CrossRefGoogle Scholar
  3. 3.
    Bruneo, D.: A stochastic model to investigate data center performance and QoS in IaaS cloud computing systems. IEEE Trans. Cloud Comput. 25(3), 560–569 (2014)Google Scholar
  4. 4.
    Chiang, Y.J., Ouyang, Y.C., Hsu, C.H.: Performance and cost-effectiveness analyses for cloud services based on rejected. IEEE Trans. Serv. Comput. 9(3), 446–455 (2016)CrossRefGoogle Scholar
  5. 5.
    Homsi, S., Liu, S., Chaparro-Baquero, A., Bai, O., Ren, S., Quan, G.: Workload consolidation for cloud data centers with guaranteed QoS using request reneging. IEEE Trans. Parallel Distrib. Syst. 28(7), 2103–2116 (2017)CrossRefGoogle Scholar
  6. 6.
    Ait El Mahjoub, Y., Fourneau, J.-M., Castel-Taleb, H.: Analysis of energy consumption in cloud center with tasks migrations. In: Gaj, P., Sawicki, M., Kwiecień, A. (eds.) CN 2019. CCIS, vol. 1039, pp. 301–315. Springer, Cham (2019). CrossRefGoogle Scholar
  7. 7.
    Mishra, S.K., Sahoo, B., Parida, P.P.: Load balancing in cloud computing: a big picture. Advances in Big Data and Cloud Computing. J. King Saud Univ. - Comput. Inf. Sci. (2018)Google Scholar
  8. 8.
    Gupta, S., Arora, S.: Queueing system in cloud services management: a survey. Int. J. Pure Appl. Math. 119(12), 12741–12753 (2018)Google Scholar
  9. 9.
    Vilaplana, J., et al.: A queueing theory model for cloud computing. J. Supercomput. 69(1), 492–507 (2014)CrossRefGoogle Scholar
  10. 10.
    Czachórski, T., Kuaban, G.S., Nycz, T.: Multichannel diffusion approximation models for the evaluation of multichannel communication networks. In: Vishnevskiy, V.M., Samouylov, K.E., Kozyrev, D.V. (eds.) DCCN 2019. LNCS, vol. 11965, pp. 43–57. Springer, Cham (2019). Scholar
  11. 11.
    Vetha, S., Devi, V.: Dynamic resource allocation in cloud using queueing model. J. Ind. Pollut. Control 33(2), 1547–1554 (2017)Google Scholar
  12. 12.
    Cheng, C., Li, J., Wang, Y.: An energy-saving task scheduling strategy based on vacation queuing theory in cloud computing. Tsinghua Sci. Technol. 20(1), 28–39 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    ElKafhali, S., Salah, K.: Modelling and analysis of performance and consumption in cloud data centers. Arab. J. Sci. Eng. 43, 7789–7802 (2018)CrossRefGoogle Scholar
  14. 14.
    Duan, Q., Yu, S., Zhang, Z.: Cloud service performance evaluation: status, challenges, and opportunities - a survey from the system modeling perspective. Digit. Commun. Netw. 3, 101–111 (2017)CrossRefGoogle Scholar
  15. 15.
    Al-Seedy, R.O., El-Sherbiny, A.A., El-Shehawy, S.A., Ammar, S.I.: Transient solution of the \(M/M/c\) queue with balking and reneging: a survey. Comput. Math. Appl. 57(8), 1280–1285 (2009)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kumar, R., Sharma, S.K.: M/M/1 feedback queueing models with retention of reneged customers and balking. Am. J. Oper. Res. 3(2A), 1–6 (2013)Google Scholar
  17. 17.
    Karina, V., Rodriguez, Q., Guillemin, F.: Performance analysis of resource pooling for network function virtualization. Psicologia: Reflexao e Crítica, Universidade Federal do Rio Grande do Sul, 2016. hal-01621281 (2016)Google Scholar
  18. 18.
    Chiang, Y., Ouyang, Y.: Profit Optimization in SLA-Aware Cloud Services with a Finite Capacity Queuing Model Mathematical Problems in Engineering. Hindawi Publishing Corporation, London (2014)Google Scholar
  19. 19.
    Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., Hieu, N.T., Tenhunen, H.: Energy-aware VM consolidation in cloud data centers using utilization prediction model. IEEE Trans. Cloud Comput. 7(2), 524–536 (2019)CrossRefGoogle Scholar
  20. 20.
    Arunarani, A., Manjula, D., Sugumaran, V.: Task scheduling techniques in cloud computing: a literature survey. Future Gener. Comput. Syst. 91, 407–415 (2019)CrossRefGoogle Scholar
  21. 21.
    Abdullahi, M., Ngadi, M.A., Abdulhamid, S.M.: Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Gener. Comput. Syst. 56, 640650 (2016)CrossRefGoogle Scholar
  22. 22.
    Wang, W., Gelenbe, E.: Adaptive dispatching of tasks in the cloud. IEEE Trans. Cloud Comput. 6(1), 33–45 (2018)CrossRefGoogle Scholar
  23. 23.
    Wei, L., Foh, C.H., He, B., Cai, J.: Towards efficient resource allocation for heterogeneous workloads in IaaS clouds. IEEE Trans. Cloud Comput. 6(1), 264–275 (2018)CrossRefGoogle Scholar
  24. 24.
    Kumar, R., Soodan, B.S.: Transient numerical analysis of a queueing model with correlated reneging, balking and feedback. Reliab.: Theory Appl. 14(4), 46–54 (2019)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Theoretical and Applied InformaticsPolish Academy of SciencesGliwicePoland
  2. 2.School of MathematicsShri Mata Vaishno Devi UniversityKatraIndia
  3. 3.Department of Computer Graphics, Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer ScienceSilesian University of TechnologyGliwicePoland

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