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A Dynamic Evolutionary Scheduling Algorithm for Cloud Tasks with High Efficiency and High QoS Satisfactions

  • Xiaoyong Guo
  • Jiantao ZhouEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1042)

Abstract

Efficient task scheduling is one of the main ways to increase cloud computing’s throughput. In cloud computing, many tasks need to be scheduled on different virtual machines to augment system throughput while satisfying QoS conditions. Task scheduling is an NP-Complete problem, especially dynamic task scheduling in heterogenous cloud environments. This paper presents a dynamic task scheduling algorithm based on heterogeneous cloud environment. The proposed algorithm uses the preponderance of Topological sort, Genetic Algorithm (GA) and NSGA-II. The experimental results show that the scheduling efficiency and QoS satisfaction of the algorithm are significantly better than GA, and the latter is one of the most commonly used heuristic optimization techniques in task scheduling problems.

Keywords

Dynamic task scheduling Evolution algorithm Task dependencies Global task priority Heterogenous cloud environment 

Notes

Acknowledgement

The research is supported by Natural Science Foundation of China under Grant No. 61662054, 61262082, Inner Mongolia Science and Technology Innovation Team of Cloud Computing and Software Engineering and Inner Mongolia Application Technology Research and Development Funding Project “Mutual Creation Service Platform Research and Development Based on Service Optimizing and Operation Integrating” under Grant 201702168, Inner Mongolia Engineering Lab of Cloud Computing and Service Software and Inner Mongolia Engineering Lab of Big Data Analysis Technology.

References

  1. 1.
    Zhao, C.-Y.: Research and Implementation of Job Scheduling Algorithm in Cloud Computing. Beijing Jiaotong University, Beijing (2009)Google Scholar
  2. 2.
    Chen, K., Zheng, W.-M.: Cloud computing: system instances and current research. J. Softw. 20, 1337–1348 (2010).  https://doi.org/10.3724/SP.J.1001.2009.03493CrossRefGoogle Scholar
  3. 3.
    Liu, X.-Q.: Research on Data Center Structure and Scheduling Mechanism in Cloud Computing. University of Science and Technology of China (2011)Google Scholar
  4. 4.
    Shen, Q., Xu, M.-Y., Chun-Mao, J.: Review of task scheduling research in cloud computing. Intell. Comput. Appl. 4, 75–77 (2014)Google Scholar
  5. 5.
    Abdullahi, M., Ngadi, M.A., Abdulhamid, S.M.: Symbiotic Organism Search optimization based task scheduling in cloud computing environment. Future Gen. Comput. Syst. 56, 640–650 (2016).  https://doi.org/10.1016/j.future.2015.08.006CrossRefGoogle Scholar
  6. 6.
    Wu, H.: Research of Task Scheduling Algorithm in the Cloud Environment. Nanjing University of Posts and Telecommunications, Nanjing (2013)Google Scholar
  7. 7.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008).  https://doi.org/10.1145/1327452.1327492CrossRefGoogle Scholar
  8. 8.
    Cui, Y., Xiaoqing, Z.: Workflow tasks scheduling optimization based on genetic algorithm in clouds. In: 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp. 6–10. IEEE, Chengdu (2018).  https://doi.org/10.1109/ICCCBDA.2018.8386458
  9. 9.
    Akbari, M., Rashidi, H., Alizadeh, S.H.: An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems. Eng. Appl. Artif. Intell. 61, 35–46 (2017).  https://doi.org/10.1016/j.engappai.2017.02.013CrossRefGoogle Scholar
  10. 10.
    Byrappa, S.D., Hegde, S.N., Rajan, M.A., Krishnappa, H.K.: A novel task scheduling scheme for computational grids - greedy approach. In: 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), pp. 1026–1033. IEEE, Krakow (2018).  https://doi.org/10.1109/AINA.2018.00149
  11. 11.
    Li, Z.-Y., Chen, S.-M., Yang, B., Li, R.-F.: Multi-objective memetic algorithm for task scheduling on heterogeneous cloud. Jisuanji Xuebao/Chinese J. Comput. 39(2), 377–390 (2016).  https://doi.org/10.11897/SP.J.1016.2016.00377
  12. 12.
    Gandhi, T., Alam, T.: Quantum genetic algorithm with rotation angle refinement for dependent task scheduling on distributed systems. In: 2017 Tenth International Conference on Contemporary Computing (IC3), pp. 1–5. IEEE, Noida (2017).  https://doi.org/10.1109/IC3.2017.8284295
  13. 13.
    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).  https://doi.org/10.1016/j.jss.2016.07.006CrossRefGoogle Scholar
  14. 14.
    Hao, L., Yang, X., Hu, S.: Task scheduling of improved time shifting based on genetic algorithm for phased array radar. In: 2016 IEEE 13th International Conference on Signal Processing (ICSP), pp. 1655–1660. IEEE, Chengdu (2016).  https://doi.org/10.1109/ICSP.2016.7878109
  15. 15.
    Shobana, G., Geetha, M., Suganthe, R.C.: Nature inspired preemptive task scheduling for load balancing in cloud datacenter. In: International Conference on Information Communication and Embedded Systems (ICICES2014), pp. 1–6. IEEE, Chennai (2014).  https://doi.org/10.1109/ICICES.2014.7033816
  16. 16.
    Zhou, J., Dong, S.-B., Tang, D.-Y.: Task scheduling algorithm in cloud computing based on invasive tumor growth optimization. Jisuanji Xuebao/Chinese J. Comput. 41(6), 1360–1375 (2018).  https://doi.org/10.11897/SP.J.1016.2018.01360
  17. 17.
    Li, J.-F., Peng, J.: Task scheduling algorithm based on improved genetic algorithm in cloud computing environment. J. Comput. Appl. 31(01), 184–186 (2011)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Hamad, S.A., Omara, F.A.: Genetic-based task scheduling algorithm in cloud computing environment. Int. J. Adv. Comput. Sci. Appl. 7 (2016).  https://doi.org/10.14569/IJACSA.2016.070471
  19. 19.
    Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D.: Cloud task scheduling based on load balancing ant colony optimization. In: 2011 Sixth Annual Chinagrid Conference, pp. 3–9. IEEE, Liaoning (2011).  https://doi.org/10.1109/ChinaGrid.2011.17
  20. 20.
    Zhang, F., Cao, J., Li, K., Khan, S.U., Hwang, K.: Multi-objective scheduling of many tasks in cloud platforms. Future Gen. Comput. Syst. 37, 309–320 (2014).  https://doi.org/10.1016/j.future.2013.09.006CrossRefGoogle Scholar
  21. 21.
    Wang, X.Y., Wei, Z.J.: Discussion on the algorithm in topological collating. J. Northwest Univ. (Nat. Sci. Edn.) (2002).  https://doi.org/10.16152/j.cnki.xdxbzr.2002.04.007
  22. 22.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002).  https://doi.org/10.1109/4235.996017CrossRefGoogle Scholar
  23. 23.
    Chen, J., Xiong, S., Lin, W.: Improved strategies and researches of NSGA-II algorithm. Comput. Eng. Appl. 47(19), 42–45 (2011)Google Scholar
  24. 24.
    Dick, R.P., Rhodes, D.L., Wolf, W.: TGFF: task graphs for free. In: Proceedings of the Sixth International Workshop on Hardware/Software Codesign, pp. 97–101. IEEE, Seattle (1998).  https://doi.org/10.1109/HSC.1998.666245

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Inner Mongolia Engineering Lab of Cloud Computing and Service Software, College of Computer ScienceInner Mongolia UniversityHohhotChina

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