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Comparative evaluation of task priorities for processing and bandwidth capacities-based workflow scheduling for cloud environment

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Abstract

With the development of the cloud computing market, cloud computing providers are offering to their users the flexibility to choose the desired capacity of both processing and data transfer (bandwidth) to use during the execution of their applications. The selected processing capacity can be shared among the number of virtual machines (VMs) that are capable of completing the user’s application within optimal time. During the execution of the users’ application, each task is scheduled on the VM that is capable of minimizing its execution time. However, the tasks are interdependent which means that the execution delay of a parent task will lead to the delay of its dependent tasks. The Bandwidth can be used to reduce the waiting time and avoid this delay of the dependent task. The execution time of the user’s application depends on some factors such as tasks’ priority, application’s structure, size, and the number of the VM selected to share the selected processing capacity. Determining the number of VM to share the users’ selected capacities under users’ specified quality of services remains a big challenge. Determining the number of VM to share the users selected processing capacity has been studied in our previous paper, where makespan idle-time was given the same weight. In this paper, we extended our previous work by adding the bandwidth capacity to the user’s selection and use CRITIC a multi-criteria decision-making technique to determine the weight of each criterion. The evaluation results show that the proposed heuristic can work well under different parameter settings.

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References

  1. cloudsigma. http://cloudsigma.com/

  2. Elastichosts. https://www.elastichosts.com/

  3. Blythe J, Jain S, Deelman E, Gil Y, Vahi K, Mandal A, Kennedy K (2005) Task scheduling strategies for workflow-based applications in grids. In: CCGrid 2005. IEEE international symposium on cluster computing and the grid, 2005., vol 2, pp 759–767. IEEE

  4. Bugingo E, Zheng W, Zhang D, Chen J (2019) Dynamic virtual machine number selection for processing-capacity constrained workflow scheduling in cloud computing environments. In: 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)

  5. Diakoulaki D, Mavrotas G, Papayannakis L (1995) Determining objective weights in multiple criteria problems: the critic method. In: Computers & Operations Research

  6. Byun EK, Kee YS, Kim JS, Deelman E, Maeng S (2011) Bts: resource capacity estimate for time-targeted science workflows. J Parallel Distrib Comput 71(6):848–862

    Article  Google Scholar 

  7. Byun EK, Kee YS, Kim JS, Maeng S (2011) Cost optimized provisioning of elastic resources for application workflows. Future Gen Comput Syst 27(8):1011–1026

    Article  Google Scholar 

  8. Cui Z, Cao Y, Cai X, Cai J, Chen J (2019) Optimal leach protocol with modified bat algorithm for big data sensing systems in internet of things. J Parallel Distrib Comput 132:217–229

    Article  Google Scholar 

  9. Cui Z, Sun B, Wang G, Xue Y, Chen J (2017) A novel oriented cuckoo search algorithm to improve dv-hop performance for cyber-physical systems. J Parallel Distrib Comput 103:42–52

    Article  Google Scholar 

  10. Garey M, Johnson D (1979) Computer and intractability: a guide to the theory of np-completeness. Freeman, New York

    MATH  Google Scholar 

  11. Genez TA, Pietri I, Sakellariou R, Bittencourt LF, Madeira ER (2015) A particle swarm optimization approach for workflow scheduling on cloud resources priced by cpu frequency. In: Proceedings of the 8th international conference on utility and cloud computing, pp 237–241. IEEE Press

  12. Huu TT, Montagnat J (2010) Virtual resources allocation for workflow-based applications distribution on a cloud infrastructure. In: Proceedings of the 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing, pp 612–617. IEEE Computer Society

  13. Joe I, Sakellariou S (2016) Cost-efficient cpu provisioning for scientific workflows on clouds. In: Altmann J, Silaghi GC, Rana OF (eds) Economics of grids, clouds, systems, and services. Springer, Cham, pp 49–64

    Google Scholar 

  14. Liaqat M, Chang V, Gani A, Hamid SHA, Toseef M, Shoaib U, Ali RL (2017) Federated cloud resource management: review and discussion. J Netw Comput Appl 77:87–105

    Article  Google Scholar 

  15. Lopes Genez TA, Sakellariou R, Bittencourt LF, Mauro Madeira ER, Braun T (2018) Scheduling scientific workflows on clouds using a task duplication approach. In: 2018 IEEE/ACM 11th international conference on utility and cloud computing (UCC), pp 83–92

  16. Masdari M, ValiKardan S, Shahi Z, Azar SI (2016) Towards workflow scheduling in cloud computing: a comprehensive analysis. J Netw Comput Appl 66:64–82

    Article  Google Scholar 

  17. Pietri I, Sakellariou R (2016) Cost-efficient cpu provisioning for scientific workflows on clouds. In: Altmann J, Silaghi GC, Rana OF (eds) Economics of grids, clouds, systems, and services. Springer, Cham, pp 49–64

    Chapter  Google Scholar 

  18. Singh S, Chana I (2016) A survey on resource scheduling in cloud computing: issues and challenges. J Grid Comput 14(2):217–264

    Article  Google Scholar 

  19. Sudarsanam A, Srinivasan M, Panchanathan S (2004) Resource estimation and task scheduling for multithreaded reconfigurable architectures. In: Proceedings. Tenth international conference on parallel and distributed systems, 2004. ICPADS 2004., pp 323–330. IEEE

  20. Thiago AL Genez IP, Sakellariou R, Bittencourt LF, Madeir ERM (2015) A particle swarm optimization approach for workflow scheduling on cloud resources priced by cpu frequency In: 2015 IEEE/ACM 8th international conference on utility and cloud computing (UCC)

  21. Topcuoglu H, Hariri S, Min-You W (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274

    Article  Google Scholar 

  22. Wang GG, Cai X, Cui Z, Min G, Chen J (2017) High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm. IEEE Trans Emerg Topics Comput 8:20–30

    Google Scholar 

  23. Wang JJ, Jing YY, Zhang CF, Zhao JH (2009) Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew Sustain Energy Rev 13(9):2263–2278

    Article  Google Scholar 

  24. Wang P, Huang J, Cui Z, Xie L, Chen J (2019) A Gaussian error correction multi-objective positioning model with nsga-ii. Concurr Comput Pract Exp 32:e5464

    Google Scholar 

  25. Wieczorek M, Podlipnig S, Prodan R, Fahringer T (2008) Bi-criteria scheduling of scientific workflows for the grid. In: 2008 Eighth IEEE international symposium on cluster computing and the grid (ccGrid), pp 9–16. IEEE

  26. Wu F, Wu Q, Tan Y (2015) Workflow scheduling in cloud: a survey. J Supercomput 71(9):3373–3418

    Article  Google Scholar 

  27. Zhang D, Yan W, Bugingo E, Zheng W, Chen J (2018) A benchmark approach and its toolkit for online scheduling of multiple deadline-constrained workflows in big-data processing systems. Future Gen Comput Syst 85:222–234

    Article  Google Scholar 

  28. Zhang M, Wang H, Cui Z, Chen J (2018) Hybrid multi-objective cuckoo search with dynamical local search. Memetic Comput 10(2):199–208

    Article  Google Scholar 

  29. Zheng W, Emmanuel B, Wang C (2015) A randomized heuristic for stochastic workflow scheduling on heterogeneous systems. In: 2015 third international conference on advanced cloud and big data, pp 88–95

  30. Zheng W, Qin Y, Bugingo E, Zhang D, Chen J (2018) Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds. Future Gen Comput Syst 82:244–255

    Article  Google Scholar 

  31. Zheng W, Yan W, Bugingo E, Zhang D (2018) Online scheduling to maximize resource utilization of deadline-constrained workflows on the cloud. In: 2018 IEEE 22nd international conference on computer supported cooperative work in design ( (CSCWD)), pp 98–103

  32. Zhihua C, Yechuang W, Xingjuan C et al (2018) A pigeon-inspired optimization algorithm for many-objective optimization problems. Sci China Inf Sci 62:70212

    Google Scholar 

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Acknowledgements

The work was supported by the National Science Foundation of Fujian Province of China (No. 2018J01107) and was also jointly supported by National Natural Science Foundation of China (NSFC, Grant No. 61671397, 61672439).

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  1. Computer Science, school of Informatics, Xiamen University, Xiamen, 361005, Fujian, China

    Emmanuel Bugingo, Zheng Wei & Zhang Defu

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  1. Emmanuel Bugingo
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  2. Zheng Wei
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  3. Zhang Defu
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Correspondence to Zheng Wei.

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Bugingo, E., Wei, Z. & Defu, Z. Comparative evaluation of task priorities for processing and bandwidth capacities-based workflow scheduling for cloud environment. J Supercomput 78, 3814–3842 (2022). https://doi.org/10.1007/s11227-021-03979-y

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