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A novel workflow scheduling with multi-criteria using particle swarm optimization for heterogeneous computing systems

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Abstract

Importance of workflow applications (WAs) is expediting in various fields of science and engineering. Scheduling of WAs is a non-deterministic polynomial-complete problem. One of the key challenges of scheduling the WAs is to create valid execution sequence. The validity of the execution sequence is ensured by preserving dependency constraints. Therefore, workflow scheduling algorithms (WSAs) are burning insight to researchers. In this paper, we have proposed a particle swarm optimization based workflow scheduling algorithm to address the problem. Our derived fitness function simultaneously considers several conflicting parameters, makespan, load-balancing, resource-utilization, and speed up ratio. The particle is represented in such a way that it produces a complete solution by preserving the dependency constraints. Moreover, the updated positions of the particles are also ensured to be valid in each iteration. The performance of the proposed work is extensively tested using several scientific WAs. Our simulation results show significant improvements in terms of the considered objectives. The effectiveness of the results is also validated using a statistical hypothesis, Analysis of Variance.

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References

  1. Rodrigo, G.P., Östberg, P.-O., Elmroth, E., Antypas, K., Gerber, R., Ramakrishnan, L.: Towards understanding hpc users and systems: a nersc case study. J. Parallel Distrib. Comput. 111, 206–221 (2018)

    Article  Google Scholar 

  2. Xu, H., Li, R., Zeng, L., Li, K., Pan, C.: Energy-efficient scheduling with reliability guarantee in embedded real-time systems. Sustain. Comput.: Inform. Syst. 18, 137–148 (2018)

    Google Scholar 

  3. Naik, N.S., Negi, A., BR, T.B., Anitha, R.: A data locality based scheduler to enhance mapreduce performance in heterogeneous environments. Future Gener. Comput. Syst. 90, 423–434 (2019)

    Article  Google Scholar 

  4. Arunarani, A., Manjula, D., Sugumaran, V.: Task scheduling techniques in cloud computing: a literature survey. Future Gener. Comput. Syst. 91, 407–415 (2019)

    Article  Google Scholar 

  5. Pegasus: Workflow Generator. https://github.com/pegasus-isi/WorkflowGenerator/. Accessed 3 Sept 2018

  6. Choudhary, A., Gupta, I., Singh, V., Jana, P.K.: A gsa based hybrid algorithm for bi-objective workflow scheduling in cloud computing. Future Gener. Comput. Syst. 83, 14–26 (2018)

    Article  Google Scholar 

  7. AlEbrahim, S., Ahmad, I.: Task scheduling for heterogeneous computing systems. J. Supercomput. 73(6), 2313–2338 (2017)

    Article  Google Scholar 

  8. Liu, Y., Zhang, C., Li, B., Niu, J.: Dems: a hybrid scheme of task scheduling and load balancing in computing clusters. J. Netw. Comput. Appl. 83, 213–220 (2017)

    Article  Google Scholar 

  9. Bose, A., Biswas, T., Kuila, P.: A novel genetic algorithm based scheduling for multi-core systems. In: 4th International Conference on Smart Innovations in Communication and Computational Sciences (SICCS), vol. 851, pp. 45–54, Springer, Berlin (2018)

  10. Gogos, C., Valouxis, C., Alefragis, P., Goulas, G., Voros, N., Housos, E.: Scheduling independent tasks on heterogeneous processors using heuristics and column pricing. Future Gener. Comput. Syst. 60, 48–66 (2016)

    Article  Google Scholar 

  11. Li, K.: Scheduling parallel tasks with energy and time constraints on multiple manycore processors in a cloud computing environment. Future Gener. Comput. Syst. 82, 591–605 (2018)

    Article  Google Scholar 

  12. Biswas, T., Kuila, P., Ray, A.K.: A novel energy efficient scheduling for high performance computing systems. In: 9th International Conference on Computing, Communication and Networking Technologies (9th ICCCNT), IEEE, pp. 1–6 (2018)

  13. Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer, Berlin (2011)

  14. Gupta, I., Kumar, M.S., Jana, P.K.: Efficient workflow scheduling algorithm for cloud computing system: a dynamic priority-based approach. Arab. J. Sci. Eng. 43(12), 7945–7960 (2018)

    Article  Google Scholar 

  15. Topcuoglu, H., Hariri, S., Wu, M.-Y.: Task scheduling algorithms for heterogeneous processors. In: Proceedings of the Eighth Heterogeneous Computing Workshop (HCW’99), 1999, pp. 3–14. IEEE (1999)

  16. Wu, C.-G., Wang, L.: A multi-model estimation of distribution algorithm for energy efficient scheduling under cloud computing system. J. Parallel Distrib. Comput. 117, 63–72 (2018)

    Article  Google Scholar 

  17. Entezari-Maleki, R., Bagheri, M., Mehri, S., Movaghar, A.: Performance aware scheduling considering resource availability in grid computing. Eng. Comput. 33(2), 191–206 (2017)

    Article  Google Scholar 

  18. Kumar, N., Vidyarthi, D.P.: A novel hybrid pso-ga meta-heuristic for scheduling of dag with communication on multiprocessor systems. Eng. Comput. 32(1), 35–47 (2016)

    Article  Google Scholar 

  19. Xu, Y., Li, K., He, L., Zhang, L., Li, K.: A hybrid chemical reaction optimization scheme for task scheduling on heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 26(12), 3208–3222 (2015)

    Article  Google Scholar 

  20. Liu, J., Li, K., Zhu, D., Han, J., Li, K.: Minimizing cost of scheduling tasks on heterogeneous multicore embedded systems. ACM Trans. Embed. Comput. Syst. (TECS) 16(2), 36 (2017)

    Google Scholar 

  21. Biswas, T., Kuila, P., Ray, A.K.: A novel scheduling with multi-criteria for high-performance computing systems: an improved genetic algorithm-based approach. Eng. Comput. 35(4), 1475–1490 (2019)

    Article  Google Scholar 

  22. Biswas, T., Kuila, P., Ray, A.K.: A novel resource aware scheduling with multi-criteria for heterogeneous computing systems. Eng. Sci. Technol. Int. J. 22(2), 646–655 (2019)

    Google Scholar 

  23. Chaudhary, D., Kumar, B.: Cloudy gsa for load scheduling in cloud computing. Appl. Soft Comput. 71, 861–871 (2018)

    Article  Google Scholar 

  24. Biswas, T., Kuila, P., Ray, A.K., Sarkar, M.: Gravitational search algorithm based novel workflow scheduling for heterogeneous computing systems. Simul. Model. Pract. Theory 96, 101932 (2019)

    Article  Google Scholar 

  25. Praveen, S.P., Rao, K.T., Janakiramaiah, B.: Effective allocation of resources and task scheduling in cloud environment using social group optimization. Arab. J. Sci. Eng. 43(8), 4265–4272 (2018)

    Article  Google Scholar 

  26. Kumar, N., Vidyarthi, D.P.: An energy aware cost effective scheduling framework for heterogeneous cluster system. Future Gener. Comput. Syst. 71, 73–88 (2017)

    Article  Google Scholar 

  27. Panda, S.K., Pande, S.K., Das, S.: Task partitioning scheduling algorithms for heterogeneous multi-cloud environment. Arab. J. Sci. Eng. 43(2), 913–933 (2018)

    Article  Google Scholar 

  28. Kaur, S., Bagga, P., Hans, R., Kaur, H.: Quality of service (QoS) aware workflow scheduling (wfs) in cloud computing: a systematic review. Arab. J. Sci. Eng. 44(4), 2867–2897 (2019)

    Article  Google Scholar 

  29. Ghobaei-Arani, M., Souri, A., Safara, F., Norouzi, M.: An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Trans. Emerg. Telecommun. Technol. https://doi.org/10.1002/ett.3770 (2019)

    Article  Google Scholar 

  30. Arif, M.S., Iqbal, Z., Tariq, R., Aadil, F., Awais, M.: Parental prioritization-based task scheduling in heterogeneous systems. Arab. J. Sci. Eng. 44(4), 3943–3952 (2019)

    Article  Google Scholar 

  31. Hoseini, F., Arani, M.G., Taghizadeh, A.: ENPP: extended non-preemptive pp-aware scheduling for real-time cloud services. Int. J. Electr. Comput. Eng. 6(5), 2291–2299 (2016)

    Google Scholar 

  32. Ghobaei-Arani, M., Rahmanian, A.A., Souri, A., Rahmani, A.M.: A moth-flame optimization algorithm for web service composition in cloud computing: simulation and verification. Softw.: Pract. Exp. 48(10), 1865–1892 (2018)

    Google Scholar 

  33. Ghobaei-Arani, M., Rahmanian, A.A., Aslanpour, M.S., Dashti, S.E.: Csa-wsc: cuckoo search algorithm for web service composition in cloud environments. Soft Comput. 22(24), 8353–8378 (2018)

    Article  Google Scholar 

  34. Jana, B., Chakraborty, M., Mandal, T.: A task scheduling technique based on particle swarm optimization algorithm in cloud environment. In: Soft Computing: Theories and Applications, pp. 525–536. Springer, Berlin (2019)

  35. Adhikari, M., Koley, S.: Cloud computing: a multi-workflow scheduling algorithm with dynamic reusability. Arab. J. Sci. Eng. 43(2), 645–660 (2018)

    Article  Google Scholar 

  36. Konak, A., Coit, D.W., Smith, A.E.: Multi-objective optimization using genetic algorithms: a tutorial. Reliab. Eng. Syst. Saf. 91(9), 992–1007 (2006)

    Article  Google Scholar 

  37. Kuila, P., Jana, P.K.: Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. Eng. Appl. Artif. Intell. 33, 127–140 (2014)

    Article  Google Scholar 

  38. Ahmad, S.G., Liew, C.S., Munir, E.U., Ang, T.F., Khan, S.U.: A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems. J. Parallel Distrib. Comput. 87, 80–90 (2016)

    Article  Google Scholar 

  39. Muller, K.E., Fetterman, B.A.: Regression and ANOVA: An Integrated Approach Using SAS Software. SAS Institute, Cary (2002)

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, National Institute of Technology, Ravangla, Sikkim, 737139, India

    Tarun Biswas & Pratyay Kuila

  2. Department of Electrical and Electronics Engineering, National Institute of Technology, Ravangla, Sikkim, 737139, India

    Anjan Kumar Ray

Authors
  1. Tarun Biswas
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  2. Pratyay Kuila
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  3. Anjan Kumar Ray
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Correspondence to Pratyay Kuila.

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Biswas, T., Kuila, P. & Ray, A.K. A novel workflow scheduling with multi-criteria using particle swarm optimization for heterogeneous computing systems. Cluster Comput 23, 3255–3271 (2020). https://doi.org/10.1007/s10586-020-03085-3

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