Doubling Runtime Estimations to Improve Performance of Backfill Algorithms in Cloud Metaschedular Considering Job Dependencies

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 174)


Job scheduling is a very challenging issue in cloud computing. Traditional backfill algorithms such as Easy and conservative are extensively used as job scheduling algorithms. Backfill algorithms require the shorter job to come forward if sufficient resources for the execution of this job are available and run in parallel with the currently running jobs provided it does not delay the next queued jobs. This technique is highly dependent on runtime estimations of job execution. Moreover in real life scenario it has seen that submitted job’s may or may not be independent to each other. In this paper we have proposed a technique that uses dynamic grouping method to consider job dependencies and doubling runtime estimation method in cloud metaschedular to improve performance of backfill algorithm. Results have shown that doubling runtime estimations can significantly improve performance of backfill scheduling algorithms provided that the runtime estimations are correct.


Cloud Computing Cloud Computing Environment Cloud User Free Node First Come First Serve 


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  1. 1.
    Lawson, B.G., Smirni, E., Puiu, D.: Self-adapting backfilling scheduling for parallel systemsGoogle Scholar
  2. 2.
    Feitelson, D.G., Weil, A.M.: Utilization and predictability in scheduling the IBM SP2 with backfilling. In: Proceedings of the First Merged International and Symposium on Parallel and Distributed Processing, Parallel Processing Symposium, IPPS/SPDP 1998, pp. 542–546 (1998)Google Scholar
  3. 3.
    Tsafrir, D., Etsion, Y., Feitelson, D.G.: Backfilling using system-generated predictions rather than user runtime estimates. IEEE Transactions on Parallel and Distributed Systems 18(6), 789–803 (2007)CrossRefGoogle Scholar
  4. 4.
    Foster, I., et al.: Cloud Computing and Grid Computing 360-Degree Compared. In: Grid Computing Environments Workshop, pp. 1–10 (2008)Google Scholar
  5. 5.
    Peixoto, M.L.M., et al.: A Metascheduler architecture to provide QoS on the cloud computing. In: 2010 IEEE 17th International Conference on Telecommunications (ICT), pp. 650–657 (2010)Google Scholar
  6. 6.
    Zaharia, M., Konwinski, A., Joseph, A.D., Katz, R., Stoica, I.: Improving map reduce performance in heterogenous environment. In: OSDI (2008)Google Scholar
  7. 7.
    Isard, M., et al.: Quincy: fair scheduling for distributed comptuing clusters. Microsoft Research, SOSP (2008) Google Scholar
  8. 8.
    Buyya, R., et al.: Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities. In: International Conference on High Performance Computing & Simulation, HPCS 2009, pp. 1–11 (2009)Google Scholar
  9. 9.
    Buyya, R.: Aneka next generation. net grid/cloud computing company (2009)Google Scholar
  10. 10.
    Sadhasivam, S., Jeya Rani, R., Nagaveni, N., Vasanth Ram, R.: Design and implementation of two level scheduler for cloud computing environment. In: International Conference on Advance in Recent Technologies in Communication and Computing (2009)Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Indian Institute of TechnologyRoorkeeIndia

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