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An Efficient Task Scheduling Strategy for DAG in Cloud Computing Environment

  • Nidhi Rajak
  • Diwakar Shukla
Conference paper
  • 16 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1097)

Abstract

Cloud computing is an active research topic in computer science and its popularity is increasing day-to-day due to the high demand of cloud in every field. Data center in cloud platform is having the number of computing resources which are interconnected with very high-speed network. These resources are accessed at the rapid speed so that minimum interaction with service provider. Task scheduling is a burning area of research in cloud environment. Here an application program is represented by directed acyclic graph (DAG). Major concerned of the task scheduling method is to reduce overall execution time. i.e., to minimize the makespan. This paper presents a new strategy for task scheduling in DAG which based on two well-known attributes critical path and static level. By using these attributes, we have developed new attributes CPS which is summation of critical path and static level. New strategy works on two phases such as task priority and resource selection. The proposed method is tested using two DAG models which shows outperformance as compared to heuristic algorithm HEFT. Comparisons have been done using some performance metrics which also gives good result of proposed method.

Keywords

Cloud computing DAG Scheduling length Critical path Speedup 

References

  1. 1.
    Deelman, E., D. Gannon, M. Shields, and I. Taylor. 2009. Workflows and e-science: an overview of workflow system features and capabilities. Future Gener. Comput. Syst. 25: 528–540.CrossRefGoogle Scholar
  2. 2.
    Xue, Shengjun, Wenling Shi, and Xiaoong Xu. 2016. A heuristic scheduling algorithm based on PSO in the cloud computing environment. International Journal of u-and e-Service, Science and Technology 9 (1): 349–362.CrossRefGoogle Scholar
  3. 3.
    Papadimitriou, C., et al. 1990. Towards an architecture independent analysis of parallel algorithms. SIAM Journal of Computing 19: 322–328.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Cao Y., C. Ro, and J. Yin. 2013. Comparison of job scheduling policies in cloud computing. In Future information communication technology and applications, vol 235, ed. H.K. Jung, J. Kim, T. Sahama, C.H. Yang. Lecture Notes in Electrical Engineering. Springer, Dordrecht (2013).Google Scholar
  5. 5.
    Topcuoglu, H., Hariri, S., and M.-Y. Wu. 2002. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems 13 (3), 260–274.Google Scholar
  6. 6.
    Kumar, M.S., I. Gupta, and P.K. Jana. 2017. Delay-based workflow scheduling for cost optimization in heterogeneous cloud system. In 2017 Tenth International Conference on Contemporary Computing (IC3), 1–6, Noida.Google Scholar
  7. 7.
    Frederic, NZanywayingoma, and Yang Yang. 2017. Effective task scheduling and dynamic resource optimization based on heuristic algorithms in cloud computing environment. KSII Transactions on Internet and Information Systems 11 (12), 5780–5802Google Scholar
  8. 8.
    Haidri, R.A., C.P. Katti, and P.C. Saxena. 2017. Cost effective deadline aware scheduling strategy for workflow applications on virtual machines in cloud computing. Journal of King Saud UniversityComputer and Information Sciences. (In Press)Google Scholar
  9. 9.
    Kwok, Y.K., and I. Ahmad. 1999. Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys 31 (4): 406–471.CrossRefGoogle Scholar
  10. 10.
    Sinnen, O. 2007. Task scheduling for parallel systems, Wiley-Interscience Publication.Google Scholar
  11. 11.
    Gupta, I., M.S. Kumar, P.K. Jana. 2018. Efficient workflow scheduling algorithm for cloud computing system: A dynamic priority-based approach. Arabian Journal for Science and Engineering.Google Scholar
  12. 12.
    llavarasan, E., P. Thambidurai, and R. Mahilmannan. 2005. Performance effective task scheduling algorithm for heterogeneous computing system. In Proceedings of ISPDC, IEEE Computer Society, 28–38.Google Scholar
  13. 13.
    Pandey, S., L. Wu, S.M. Guru, and R. Buyya. 2010. A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In 24th IEEE international conference on Advanced Information Networking and Applications (AINA), 400–407, IEEE.Google Scholar
  14. 14.
    Muhammad Fasil Akbar, Ehsan Ullah Munir et al. 2016. List-based task scheduling for cloud computing, 2016, IEEE International Conference on Internet of Things and IEEE Green Computing and Communication (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Samrt Data (SmartData).Google Scholar
  15. 15.
    M. F. Akbar, E. U. Munir, M. M. Rafique, Z. Malik, S. U. Khan and L. T. Yang. 2016. List-based task scheduling for cloud computing, 2016, IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Nidhi Rajak
    • 1
  • Diwakar Shukla
    • 1
  1. 1.Department of Computer Science and ApplicationsDr. Harisingh Gour Central UniversitySagarIndia

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