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IRICT 2019: Emerging Trends in Intelligent Computing and Informatics pp 227–234Cite as

Convergence-Based Task Scheduling Techniques in Cloud Computing: A Review

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1073))

Abstract

The cloud computing promises various benefits that are striking to establishments and consumers of their services. These benefits encourage more business establishments, institutes, and users in need of computing resources to move to the cloud because of efficient task scheduling. Task scheduling is a means by which the tasks or job specified by users are mapped to the resources that execute them. Task scheduling problems in cloud, has been considered as a hard Nondeterministic Polynomial time (Np-hard) optimization problem. Task Scheduling is use to map the task to the available cloud resources like server, CPU memory, storage, and bandwidth for better utilization of resource in cloud. Some of the problems in the task scheduling include load-balancing, low convergence issues, makespan, etc. Convergence in task scheduling signifies a point in the search space that optimize an objective function. The non-independent tasks has been scheduled based on some parameters which includes makespan, response time, throughput and cost. In this paper, an extensive review on existing convergence based task scheduling techniques was carried out spanning through 2015 to 2019. This review would provide clarity on the current trends in task scheduling techniques based on convergence issues and the problem solved. It is intended to contribute to the prevailing body of research and will assist the researchers to gain more knowledge on task scheduling in cloud based on convergence issues.

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References

  1. Rani, B.K., Rani, B.P., Babu, A.V.: Cloud computing and inter-clouds-types, topologies and research issues. Proc. Comput. Sci. 50, 24–29 (2015)

    Article  Google Scholar 

  2. Cheng, M., Li, J., Nazarian, S.: DRL-cloud: deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers. In: Proceedings of Asia South Pacific Design Automation Conference ASP-DAC, January 2018, pp. 129–134 (2018)

    Google Scholar 

  3. Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing : state-of-the-art and research challenges, pp. 7–18 (2010)

    Google Scholar 

  4. Mell, T., Grance, P.: The NIST Definition of Cloud Computing (2009)

    Google Scholar 

  5. Jarraya, Y., et al.: Securing the cloud, Ericsson Rev. English Ed., vol. 95, no. 2, pp. 38–47, 2017

    Google Scholar 

  6. Sasikala, P.: Research challenges and potential green technological applications in cloud computing. Int. J. Cloud Comput. 2(1), 1–19 (2013)

    Article  Google Scholar 

  7. Alkhater, N., Walters, R., Wills, G.: Telematics and informatics an empirical study of factors in fluencing cloud adoption among private sector organisations. Telemat. Inform. 35(1), 38–54 (2018)

    Article  Google Scholar 

  8. Rabai, L.B.A., Jouini, M., Ben Aissa, A., Mili, A.: A cybersecurity model in cloud computing environments. J. King Saud Univ.-Comput. Inf. Sci., 25(1), 63–75 (2013)

    Google Scholar 

  9. Kratzke, N., Quint, P.: Understanding cloud-native applications after 10 years of cloud computing-a systematic mapping study. J. Syst. Softw. 126, 1–16 (2017)

    Article  Google Scholar 

  10. Arianyan, E., Taheri, H., Sharifian, S.: Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers. Comput. Electr. Eng. 47, 222–240 (2015)

    Article  Google Scholar 

  11. Zhou, J., Yao, X.: Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing. Appl. Soft Comput. J. 56, 379–397 (2017)

    Article  Google Scholar 

  12. Singh, P., Dutta, M., Aggarwal, N.: A review of task scheduling based on meta-heuristics approach in cloud computing. Knowl. Inf. Syst. 52(1), 1–51 (2017)

    Google Scholar 

  13. Achar, R., Thilagam, P.S., Shwetha, D., Pooja, H.: Optimal scheduling of computational task in cloud using virtual machine tree. In: 2012 Third International Conference Emerging Application Information Technology, pp. 143–146 (2012)

    Google Scholar 

  14. Kalra, M., Singh, S.: A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inf. J. 16(3), 275–295 (2015)

    Article  Google Scholar 

  15. Abdulhamid, S.M., Latiff, M.S.A., Madni, S.H.H., Oluwafemi, O.: A survey of league championship algorithm: prospects and challenges. Indian J. Sci. Technol. 8(February), 101–110 (2015)

    Article  Google Scholar 

  16. Gabi, D., Samad, A., Zainal, A.: Systematic review on existing load balancing techniques in cloud computing. Int. J. Comput. Appl. 125(9), 16–24 (2015)

    Google Scholar 

  17. Madni, S.H.H., Latiff, M.S.A., Coulibaly, Y., Abdulhamid, S.M.: Recent advancements in resource allocation techniques for cloud computing environment: a systematic review. Cluster Comput. 20(3), 2489–2533 (2017)

    Article  Google Scholar 

  18. Kumar, P., Kumar, R.: Issues and challenges of load balancing techniques in cloud computing. ACM Comput. Surv. 51(6), 1–35 (2019)

    Article  Google Scholar 

  19. Abdullahi, M., Ngadi, M.A.: Hybrid symbiotic organisms search optimization algorithm for scheduling of tasks on cloud computing environment. PLoS ONE 11(6), 1–29 (2016)

    Article  Google Scholar 

  20. Keshanchi, B., Souri, A., Navimipour, N.J.: An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J. Syst. Softw. 124, 1–21 (2017)

    Article  Google Scholar 

  21. Dordaie, N., Navimipour, N.J.: A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments. ICT Express 4(4), 199–202 (2018)

    Article  Google Scholar 

  22. Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A.K.: Innovations in bio-inspired computing and applications. In: Proceedings of the 6th international Conference on Innovations in Bio-inspired Computing and Applications (IBICA 2015), Kochi, India, 16–18 December 2015. Advances in Intelligent System and Computing, vol. 424 (2016)

    Google Scholar 

  23. Junwei, G., Shuo, S., Yiqiu, F.: Cloud resource scheduling algorithm based on improved LDW particle swarm optimization algorithm. In: Proceedings of 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference ITOEC 2017, January 2017, pp. 669–674 (2017)

    Google Scholar 

  24. Vairam, T., Sarathambekai, S., Umamaheswari, K.: Multiprocessor task scheduling problem using hybrid discrete particle swarm optimization. Sadhana - Acad. Proc. Eng. Sci. 43(12), 1–13 (2018)

    MathSciNet  Google Scholar 

  25. Xie, Y., et al.: A novel directional and non-local-convergent particle swarm optimization based workflow scheduling in cloud–edge environment. Future Gener. Comput. Syst. 97, 361–378 (2019)

    Article  Google Scholar 

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Acknowledgment

This work was sponsored by the Nigerian Tertiary Education Trust Fund (TETFund) in collaboration with Kogi State Polytechnic Lokoja, Nigeria.

Author information

Authors and Affiliations

  1. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Malaysia

    Ajoze Abdulraheem Zubair, Shukor Bin Abd Razak, Md. Asri Bin Ngadi, Aliyu Ahmed & Syed Hamid Hussain Madni

  2. Kogi State Polytechnic, P.M.B 1101, Lokoja, Kogi state, Nigeria

    Ajoze Abdulraheem Zubair

Authors
  1. Ajoze Abdulraheem Zubair
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  2. Shukor Bin Abd Razak
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  3. Md. Asri Bin Ngadi
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  4. Aliyu Ahmed
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  5. Syed Hamid Hussain Madni
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Corresponding author

Correspondence to Ajoze Abdulraheem Zubair .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. School of Computing, Universiti Utara Malaysia (UUM), Sintok, Kedah Darul Aman, Malaysia

    Fathey Mohammed

  3. Management of Information Systems Department College of Business Administration, Taibah University, Yanbu, Saudi Arabia

    Nadhmi Gazem

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Zubair, A.A., Razak, S.B.A., Ngadi, M.A.B., Ahmed, A., Madni, S.H.H. (2020). Convergence-Based Task Scheduling Techniques in Cloud Computing: A Review. In: Saeed, F., Mohammed, F., Gazem, N. (eds) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-33582-3_22

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