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DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing

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Abstract

Cloud infrastructures are suitable environments for processing large scientific workflows. Nowadays, new challenges are emerging in the field of optimizing workflows such that it can meet user’s service quality requirements. The key to workflow optimization is the scheduling of workflow tasks, which is a famous NP-hard problem. Although several methods have been proposed based on the genetic algorithm for task scheduling in clouds, our proposed method is more efficient than other proposed methods due to the use of new genetic operators as well as modified genetic operators and the use of load balancing routine. Moreover, a solution obtained from a heuristic used as one of the initial population chromosomes and an efficient routine also used for generating the rest of the primary population chromosomes. An adaptive fitness function is used that takes into account both cost and makespan. The algorithm introduced in this paper utilizes a load balancing routine to maximize resources’ efficiency at execution time. The performance of the proposed algorithm is evaluated by comparing the results with state of the art algorithms of this field, and the results indicate that the proposed algorithm has remarkable superiority in comparison to other algorithms and performs task scheduling with the least makespan and cost.

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References

  1. Atkinson, M., et al.: The DATA Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business. Wiley, Hoboken (2013)

    Book  Google Scholar 

  2. Bokhari, M.U., Makki, Q., Tamandani, Y.K.: A Survey on Cloud Computing, pp. 149–164. Springer, Singapore (2018)

    Google Scholar 

  3. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(6), 599–616 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Rodriguez, M.A., Buyya, R.: Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)

    Article  Google Scholar 

  6. Yuan, H., Liu, H., Bi, J., Zhou, M.: Revenue and energy cost-optimized biobjective task scheduling for Green Cloud Data Centers. IEEE Trans. Autom. Sci. Eng. (2020). https://doi.org/10.1109/TASE.2020.2971512

    Article  Google Scholar 

  7. Cui, Y., Xiaoqing, Z.: Workflow tasks scheduling optimization based on genetic algorithm in clouds. In: 2018 3rd IEEE International Conference on Cloud Computing and Big Data Analysis, pp. 6–10. ICCCBDA, Chengdu (2018)

    Google Scholar 

  8. Liu, L., Zhang, M., Buyya, R., Fan, Q.: Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing. Concurr. Comput. Pract. Exp. 29(5), e3942 (2017)

    Article  Google Scholar 

  9. Wang, X., Yeo, C.S., Buyya, R., Su, J.: Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm. Futur. Gener. Comput. Syst. 27(8), 1124–1134 (2011)

    Article  Google Scholar 

  10. Xu, Y., Li, K., Hu, J., Li, K.: A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf. Sci. (Ny) 270, 255–287 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Li, H., Wang, L., Liu, J.: Task scheduling of computational grid based on particle Swarm Algorithm. In: 2010 Third International Joint Conference on Computational Science and Optimization, pp. 332–336. IEEE, Piscataway (2010)

    Chapter  Google Scholar 

  12. Basu, S., et al.: An intelligent/cognitive model of task scheduling for IoT applications in cloud computing environment. Futur. Gener. Comput. Syst. 88, 254–261 (2018)

    Article  Google Scholar 

  13. Kimpan, W., Kruekaew, B.: Heuristic task scheduling with artificial bee colony algorithm for virtual machines. In: Proceedings – 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and: 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016, pp. 281–286. Piscataway, IEEE (2016)

    Google Scholar 

  14. Wang, J., Li, X., Ruiz, R., Yang, J., Chu, D.: Energy Utilization Task Scheduling for MapReduce in Heterogeneous Clusters, IEEE Transactions on Services Computing. Piscataway, IEEE (2020)

    Google Scholar 

  15. Sun, H., Yu, H., Fan, G.: Contract-Based Resource Sharing for Time Effective Task Scheduling in Fog-Cloud Environment. IEEE Transactions on Network and Service Management. IEEE, Piscataway (2020)

    Google Scholar 

  16. Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S.U., Li, K.: An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment. J. Grid Comput. 14(1), 55–74 (2016)

    Article  Google Scholar 

  17. Yadav, R., Zhang, W., Li, K., Liu, C., Shafiq, M., Karn, N.K.: An adaptive heuristic for managing energy consumption and overloaded hosts in a cloud data center. Wirel. Networks 26(3), 1905–1919 (2020)

    Article  Google Scholar 

  18. “MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing.” [Online]. Available: https://www.hindawi.com/journals/wcmc/2017/6741972/. Accessed: 21-Apr 2020

  19. Li, H., Zhu, G., Cui, C., Tang, H., Dou, Y., He, C.: Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing. Computing 98(3), 303–317 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  20. Garg, R., Mittal, M., Son, L.H.: Reliability and energy efficient workflow scheduling in cloud environment. Cluster Comput. 22(4), 1283–1297 (2019)

    Article  Google Scholar 

  21. Arabnejad, V., Bubendorfer, K.: Cost effective and deadline constrained scientific workflow scheduling for commercial clouds. In: Proceedings - 2015 IEEE 14th International Symposium on Network Computing and Applications, NCA 2015, pp. 106–113. Piscataway, IEEE (2016)

    Google Scholar 

  22. 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 

  23. 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. 31(2), e3770 (2020)

    Google Scholar 

  24. Mortazavi-Dehkordi, M., Zamanifar, K.: Efficient deadline-aware scheduling for the analysis of Big Data streams in public Cloud. Cluster Comput. 23(1), 241–263 (2020)

    Article  Google Scholar 

  25. Kaur, G., Kalra, M.: Deadline constrained scheduling of scientific workflows on cloud using hybrid genetic algorithm. In: Proceedings of the 7th International Conference Confluence 2017 on Cloud Computing, Data Science and Engineering, pp. 276–280. IEEE, Piscataway (2017)

    Google Scholar 

  26. Lam, A.Y.S., Li, V.O.K.: Chemical-Reaction-Inspired Metaheuristic for Optimization. IEEE Trans. Evol. Comput. 14(3), 381–399 (2010)

    Article  Google Scholar 

  27. Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)

    Article  Google Scholar 

  28. 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 

  29. 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 

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

    Article  Google Scholar 

  31. Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Futur. Gener. Comput. Syst. 29(3), 682–692 (2013)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Electronic and Computer Engineering, Department of Computer Engineering and Information Technology, Graduate University of Advanced Technology, Kerman, Iran

    Amir Iranmanesh & Hamid Reza Naji

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  1. Amir Iranmanesh
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  2. Hamid Reza Naji
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Correspondence to Amir Iranmanesh.

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Iranmanesh, A., Naji, H.R. DCHG-TS: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing. Cluster Comput 24, 667–681 (2021). https://doi.org/10.1007/s10586-020-03145-8

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  • DOI: https://doi.org/10.1007/s10586-020-03145-8

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