Advertisement

Study of Various Scheduling Procedures in Cloud Computing and Their Challenges

  • R. Anantha Kumar
  • K. Kartheeban
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)

Abstract

Distributed computing is a virtual pool of resources which are given to clients by means of Web. The principle base of distributed computing is the reflection and virtualization. There is a prerequisite of arrangements to pact through the tremendous measure of information, transmission cost, load balancing in addition to the execution of such measure of information. Cloud computing service providers one of the real objectives is to utilize the resources productively for their ideal utilize. This prompts task scheduling as a center and testing issue in cloud computing procedure. It becomes important to use the accessible resources proficiently keeping in mind the end goal to greatest benefits with advanced scheduling algorithms. My paper examines a portion of the scheduling algorithms in cloud computing and the different difficulties looked by the associations utilizing them.

Keywords

Distributed computing Scheduling Algorithm 

References

  1. 1.
    Rana, M.S., Sendhil Kumar, K.S., Jaisankar, N.: Comparison of probabilistic enhancement algorithms for resource scheduling for distributed computing condition. Glob. Diary Des. Innov. (IJET) 5(2), 1419–1427 (2013)Google Scholar
  2. 2.
    Niu, B., Martin, P., Powley, W., et al.: Workload adaptation in autonomic DBMSs. In: Procedures of CASCON 2006, Toronto, Canada, pp. 161–173. 16–19 October 2006Google Scholar
  3. 3.
    Lin, C., Lu, S.: Scheduling logical work processes flexibly for distributed computing. In: 2011 IEEE Universal Meeting on Cloud Computing (CLOUD), IEEE (2011)Google Scholar
  4. 4.
    Zhang, Q., Cheng, L., Boutaba, R.: Distributed computing: cutting edge and research challenges. J. Web. Serv. Appl. 1, 7–18 (2010)Google Scholar
  5. 5.
    Pandey, S., Wu, L., Master, S.M., et al.: A molecule swarm optimization based heuristic for scheduling work process applications in cloud computing environments. In: 2010 24th IEEE Global Gathering on Propelled Data Systems Administration and Applications (AINA), pp. 400–407, IEEE (2010)Google Scholar
  6. 6.
    Pandey, S., Wu, L., Guru, S.M., Buyya, R.: A molecule swarm improvement based heuristic for scheduling work process applications in distributed computing conditions, September 2012Google Scholar
  7. 7.
    Armbrust, A.: A perspective of distributed computing. In: Correspondences of the ACM, USA, vol.53, no. 4, pp. 50–58. April 2010Google Scholar
  8. 8.
    Gupta, D., Cherkasova, L., Gardner, R., Vahdat, A.: Enforcing execution disengagement crosswise over virtual machines in XEN. In: Procedures of the Seventh ACM/IFIP/USENIX International Conference on Middleware (Middleware 2006), pp. 342–362 (2006)Google Scholar
  9. 9.
    Cierniak, M., Li, W., Zaki, M.J.: Circle scheduling for heterogeneity. In: Proceedings of the Fourth IEEE International Symposium on Elite Dispersed Registering, pp. 78–85 (1995)Google Scholar
  10. 10.
    Wang, L., von Laszewski, G., Younge, A., He, X., Kunze, M., Tao, J., Fu, C.: Distributed computing: a point of view study. New Age Regist. 28(2), 137–146 (2010)zbMATHGoogle Scholar
  11. 11.
    Lee, Y.C., Wang, C., Zomaya, A.Y., Zhou, B.: Profit-driven administration ask for scheduling in mists. In: 2010 Tenth IEEE/ACM Global Gathering on Group, Cloud and Lattice Figuring (2011)Google Scholar
  12. 12.
    Zhang, Q., Cheng, L., Boutaba, R.: Distributed computing: cutting edge and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)CrossRefGoogle Scholar
  13. 13.
    Sureja, N.M., Chawda, B.V.: Irregular voyaging salesperson issue utilizing SA. Glob. Diary Dev. Innov. Propelled Des. 2(3), 621–624 (2012)Google Scholar
  14. 14.
    Darbha, S., Agrawal, D.P.: An task replication based adaptable scheduling algorithm for conveyed memory frameworks. Diary Parallel Circ. Fig. 46(1), 15–27 (1997)Google Scholar
  15. 15.
    Clark, C., et al.: Live relocation of virtual machines. In: Proceedings of the Second Meeting on Symposium on Arranged Frameworks Outline and Execution, vol. 2, p. 286. USENIX Affiliation (2005)Google Scholar
  16. 16.
    Cultivate, I., Kesselman, C. (eds.): The Network 2: Outline for Another Figuring Foundation. Morgan Kaufmann Distributers, San Francisco (2003). P. LIU, distributed computing. Distributing Place of Electronics Industry (2011)Google Scholar
  17. 17.
    Borges, H.P., de Souza, J.N., Schulze, B., Mury, A.R.: Programmed age of stages in distributed computing. In: Procedures of the IEEE System Tasks and Administration Symposium (NOMS 12), pp. 1311–1318 (2012)Google Scholar
  18. 18.
    Ghemawat, S.: MapReduce: improved information preparing on substantial groups. In: USENIX Symposium on Working Frameworks Outline and Execution, San Francisco, pp. 137–150, December (2004)Google Scholar
  19. 19.
    Cultivate, I., Zhao, Y., Raicu, I., Lu, S.: Distributed computing and framework processing 360-degree looked at 2008. In: Lattice Registering Situations Workshop, GCE 2008, pp. 1–10 (2008)Google Scholar
  20. 20.
    Cultivate, I., Furthermore, I., Kesselman, C.: The Framework 2: Blueprint for Another Processing Foundation. Second Version. Morgan Kaufmann Press, Burlington (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of CSEKalasalingam Institute of TechnologyKrishnankoilIndia
  2. 2.Department of CSEKalasalingam Academy of Research and EducationKrishnankoilIndia

Personalised recommendations