Static Task Scheduling Heuristic Approach in Cloud Computing Environment

  • Biswajit NayakEmail author
  • Sanjay Kumar Padhi
  • Prasant Kumar Pattnaik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)


Scheduling is to assign a resource with a starting and ending time. Mapping refers to the assigning resource to the task without specifying the start time. Mapping can be possible in several conditions. Mapping can be possible when you know what tasks are scheduled or when you do not know what tasks are scheduled. If it is known then it only requires to choose the way so that it can be mapped correctly otherwise it needs to consider varying circumstances. The proposed article focuses on the way to choose an algorithm for mapping when the tasks are scheduled. This article also analyzes experimentally the different algorithms to get out the best of it in different conditions.


Computing Datacenter Task scheduling Algorithm Makespan 


  1. 1.
    I.A. Mohialdeen, Comparative study of scheduling algorithms in cloud computing environment. J. Comput. Sci. 9(2), 252–263 (2013)CrossRefGoogle Scholar
  2. 2.
    B. Nayak, S.K. Padhi, P.K. Pattnaik, Impact of cloud accountability on clinical architecture and acceptance of health care system, in 6th International Conference on Frontiers of Intelligent Computing: Theory and Applications (Springer, 2018), pp. 149–157. Scholar
  3. 3.
    P.K. Suri, S. Rani, Design of task scheduling model for cloud applications in multi cloud environment, in ICICCT 2017, CCIS 750 (2017), pp. 11–24. Scholar
  4. 4.
    D.W. Brinkerhoff, Accountability and health systems: toward conceptual clarity and policy relevance. Health Policy Plann. 19(6), 371–379 ©Oxford University Press, 2004; all rights reserved Scholar
  5. 5.
    P. Banga, S.P. Rana, Heuristic based independent task scheduling techniques in cloud computing: a review. Int. J. Comput. Appl. (0975–8887) 166(1) (2017)CrossRefGoogle Scholar
  6. 6.
    B. Nayak, S.K. Padhi, P.K. Pattnaik, Understanding the mass storage and bringing accountability, in National Conference on Recent Trends in Soft Computing & It’s Applications (RTSCA) (2017), pp. 28–35, ISSN: 2319 – 6734Google Scholar
  7. 7.
    D. Le, V. Bhateja, G.N. Nguyen, A parallel max-min ant system algorithm for dynamic resource allocation to support QoS requirements, in 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON) (2017), pp. 697–700Google Scholar
  8. 8.
    L. Bao, D.-N. Le, G.N. Nguyen, V. Bhateja, S.C. Satapathy, Optimizing feature selection in video-based recognition using Max-Min Ant System for the online video contextual advertisement user-oriented system. J. Comput. Sci. 21, 361–370 (2017)CrossRefGoogle Scholar
  9. 9.
    S. Singh, M. Kalra, Task scheduling optimization of independent tasks in cloud computing using enhanced genetic algorithm. Int. J. Appl. Innov. Eng. Manage. (IJAIEM) 3(7), 286–291 (2014). ISSN 2319 – 4847Google Scholar
  10. 10.
    N.M. Reda, An improved sufferage meta-task scheduling algorithm in grid computing systems. Int. J. Adv. Res. 3(10), 123–129 (2015). ISSN 2320-5407Google Scholar
  11. 11.
    E.K. Tabak, B.B. Cambazoglu, C. Aykanat, Improving the performance of independent task assignment heuristics minmin, maxmin and sufferage. IEEE Trans. Parallel Distrib. Syst. 25(5), 1244–1256 (2014)CrossRefGoogle Scholar
  12. 12.
    E. Kumari, A. Monika, Review on task scheduling algorithms in cloud computing. Int. J. Sci. Environ. Technol. 4(2), 433–439 (2015). ISSN 2278-3687Google Scholar
  13. 13.
    T. Mathew, K.C. Sekaran, J. Jose, Study and analysis of various task scheduling algorithms in the cloud computing environment, in International Conference on Advances in Computing, Communications and Informatics (ICACCI) (IEEE, 2014), pp. 658–664. 978-1-4799-3080-7/14/$31.00_©2014Google Scholar
  14. 14.
    N.S. Jain, Task scheduling in cloud computing using genetic algorithm. Int. J. Comput. Sci. Eng. Inf. Technol. Res. (IJCSEITR) 6(4) (2016). SSN(P): 2249-6831; ISSN(E): 2249-7943Google Scholar
  15. 15.
    R.K. Devi1, K.V. Devi, S. Arumugam, Dynamic batch mode cost-efficient independent task scheduling scheme in cloud computing. Int. J. Adv. Soft Comput. Appl. 8(2) (2016) (ISSN 2074-8523)Google Scholar
  16. 16.
    R.M. Singh, S. Paul, A. Kumar, Task scheduling in cloud computing: review. Int. J. Comput. Sci. Inf. Technol. 5(6), 7940–7944 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Biswajit Nayak
    • 1
    Email author
  • Sanjay Kumar Padhi
    • 1
  • Prasant Kumar Pattnaik
    • 2
  1. 1.Computer Science and EngineeringBiju Patnaik Technical UniversityRourkelaIndia
  2. 2.School of Computer Science and EngineeringKalinga Institute of Industrial Technology UniversityBhubaneswarIndia

Personalised recommendations