Advertisement

Clustered Queuing Model for Task Scheduling in Cloud Environment

  • Sridevi S.
  • Rhymend Uthariaraj V.
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 645)

Abstract

With the advent of big data and Internet of Things (IoT), optimal task scheduling problem for heterogeneous multi-core virtual machines (VMs) in cloud environment has garnered greater attention from researchers around the globe. The queuing model used for optimal task scheduling is to be tuned according to the interactions of the task with the cloud resources and the availability of cloud processing entities. The queue disciplines such as First-In First-Out, Last-In First-Out, Selection In Random Order, Priority Queuing, Shortest Job First, Shortest Remaining Processing Time are all well-known queuing disciplines applied to handle this problem. We propose a novel queue discipline which is based on k-means clustering called clustered queue discipline (CQD) to tackle the above-mentioned problem. Results show that CQD performs better than FIFO and priority queue models under high demand for resource. The study shows that: in all cases, approximations to the CQD policies perform better than other disciplines; randomized policies perform fairly close to the proposed one and, the performance gain of the proposed policy over the other simulated policies, increase as the mean task resource requirement increases and as the number of VMs in the system increases. It is also observed that the time complexity of clustering and scheduling policies is not optimal and hence needs to be improved.

Keywords

Cloud computing Load balancing Clustering Queuing discipline 

Notes

Acknowledgements

We acknowledge Visvesvaraya PhD scheme for Electronics and IT, DeitY, Ministry of Communications and IT, Government of India’s fellowship grant through Anna University, Chennai for their support throughout the working of this paper.

References

  1. 1.
    Boxma, O.J.: M/G/∞ tandem queues. Stoch. Process. Appl. 18, 153–164 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Sztrik, J.: Basic Queueing Theory. University of Debrecen (2012)Google Scholar
  3. 3.
    Buyya, R., Sukumar, K.: Platforms for Building and Deploying Applications for Cloud Computing, pp. 6–11. CSI Communication (2011)Google Scholar
  4. 4.
    Xiong, K., Perros, H.: Service performance and analysis in cloud computing. In: Proceedings of the 2009 Congress on Services—I, Los Alamitos, CA, USA, pp. 693–700 (2009)Google Scholar
  5. 5.
    Ma, B.N.W.: Mark. J.W.: Approximation of the mean queue length of an M/G/c queueing system. Oper. Res. 43, 158–165 (1998)CrossRefGoogle Scholar
  6. 6.
    Miyazawa, M.: Approximation of the queue-length distribution of an M/GI/s queue by the basic equations. J. Appl. Probab. 23, pp. 443–458 (1986)Google Scholar
  7. 7.
    Yao, D.D.: Refining the diffusion approximation for the M/G/m queue. Oper. Res. 33, 1266–1277 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Tijms, H.C., Hoorn, M.H.V., Federgru, A.: Approximations for the steady-state probabilities in the M = G=c queue. Adv. Appl. Probab. 13, 186–206 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Kimura, T.: Diffusion approximation for an M = G=m queue. Oper. Res. 31, 304–321 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Vilaplana, Jordi, Solsona, Francesc, Teixidó, Ivan, Mateo, Jordi, Abella, Francesc, Rius, Josep: A queuing theory model for cloud computing. J Supercomput. 69(1), 492–507 (2014)CrossRefGoogle Scholar
  11. 11.
    Boxma, O.J., Cohen, J.W., Huffel, N.: Approximations of the Mean waiting time in an M = G=s queueing system. Oper. Res. 27, 1115–1127 (1979)CrossRefzbMATHGoogle Scholar
  12. 12.
    Kleinrock, L.: Queueing Systems: Theory, vol. 1. Wiley-Interscience, New York (1975)zbMATHGoogle Scholar
  13. 13.
    Adan, I.J.B.F., Boxma, O.J., Resing, J.A.C.: Queueing models with multiple waiting lines. Queueing Syst Theory Appl 37(1), 65–98 (2011)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Wolff, R.W.: Poisson arrivals see time averages. Oper. Res. 30, 223–231 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Cohen, J.W.: The multiple phase service network with generalized processor sharing. Acta Informatica 12, 245–284 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Khazaei, H., Misic, J., Misic, V.: Performance analysis of cloud computing centers using M/G/m/m + r. Queuing Systems. IEEE Trans. Parallel Distrib. Syst. 23(5) (2012)Google Scholar
  17. 17.
    Slothouber, L.: A model of web server performance. In: Proceedings of the Fifth International World Wide Web Conference (1996)Google Scholar
  18. 18.
    Yang, B., Tan, F., Dai, Y., Guo, S.: Performance evaluation of cloud service considering fault recovery. In: Proceedings of the First International Conference on Cloud, Computing (CloudCom’09), pp. 571–576 (2009)Google Scholar
  19. 19.
    Borst, S., Boxma, O.J., Hegde, N.: Sojourn times in finite-capacity processor-sharing queues. Next Gener. Internet Netw IEEE 55–60 (2005)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Anna UniversityChennaiIndia

Personalised recommendations