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A hybrid job scheduling algorithm based on Tabu and Harmony search algorithms

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Abstract

Nowadays, cloud computing became a very important way of processing very large and complicated jobs and services. To serve millions of users with a high satisfaction regarding cost and time requires a powerful way to schedule cloud users’ jobs. Job scheduling in a cloud computing environment is an NP-hard problem. Many scheduling algorithms have been proposed by researchers to solve such a complicated problem. In this research, a hybrid Tabu–Harmony task scheduling algorithm in cloud computing is proposed, and the proposed algorithm combines the benefits of both the Tabu search and the Harmony search algorithms in order to enhance the quality of the results. The proposed algorithm is evaluated in terms of throughput, makespan and total cost and achieved a better result compared to Tabu search, Harmony search and round-robin in terms of makespan and cost.

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References

  1. Kaur J, Sehrawat A, Bishnoi MN (2014) Survey paper on basics of cloud computing and data security. Int J Comput Sci Trends Technol 2(3):16–19

    Google Scholar 

  2. Jain P, Singhal S (2019) Cloud services models and its security features. Res Rev J Embed Syst Appl 6(3):13–17

    Google Scholar 

  3. Sareen P (2013) Cloud computing: types, architecture, applications, concerns, virtualization and role of it governance in cloud. Int J Adv Res Comput Sci Softw Eng 3:533–538

    Google Scholar 

  4. Kamboj S, Ghumman NS (2016) A survey on cloud computing and its types. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, pp 2971–2974

  5. Onugu BAN (2005) Small and medium enterprises (SMEs) in Nigeria: problems and prospects. St. Clements University Dissertations and Theses. Retrieved from http://stclements.edu/grad/gradonug.pdf

  6. Jiang YS, Chen WM (2015) Task scheduling for grid computing systems using a genetic algorithm. J Supercomput 71(4):1357–1377

    Article  Google Scholar 

  7. Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of big data on cloud computing: review and open research issues. Inf Syst 47:98–115

    Article  Google Scholar 

  8. Chen L, Li X (2017) Cloud workflow scheduling with hybrid resource provisioning. J Supercomput 74(12):1–25

    Google Scholar 

  9. Thomas A, Krishnalal G, Raj VJ (2015) Credit based scheduling algorithm in cloud computing environment. Procedia Comput Sci 46:913–920

    Article  Google Scholar 

  10. Kumar R, Sahoo G (2014) Cloud computing simulation using cloudSim. arXiv preprint arXiv:1403.3253

  11. Masadeh R, Sharieh A, Mahafzah B (2019) Humpback whale optimization algorithm based on vocal behavior for task scheduling in cloud computing. Int J Adv Sci Technol 13(3):121–140

    Google Scholar 

  12. Alsmady A, Al-Khraishi T, Mardini W, Alazzam H, Khamay-seh Y (2019) Workflow scheduling in cloud computing using memetic algorithm. In: JEEIT 2019. IEEE

  13. Madni SHH, Latiff MSA, Abdullahi M, Usman MJ (2017) Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS ONE 12(5):e0176321

    Article  Google Scholar 

  14. Kumar P, Verma A (2012) Independent task scheduling in cloud computing by improved genetic algorithm. Int J Adv Res Comput Sci Softw Eng 2(5):111–114

    MathSciNet  Google Scholar 

  15. Krishnadoss P, Jacob P (2018) OCSA: task scheduling algorithm in cloud computing environment. Int J Intell Eng Syst 11(3):271–279

    Google Scholar 

  16. Abdi S, Motamedi SA, Sharifian S (2014) Task scheduling using modified PSO algorithm in cloud computing environment. In: International Conference on Machine Learning, Electrical and Mechanical Engineering, pp 8–9

  17. Tsai CW, Rodrigues JJ (2014) Metaheuristic scheduling for cloud: a survey. IEEE Syst J 8(1):279–291

    Article  Google Scholar 

  18. Rashmi KS, Suma V, Vaidehi M (2012) Factors influencing job rejections in cloud environment. In: International Joint Conference on Emerging Intelligent Sustainable Technologies (Bangalore)

  19. Tawfeek MA, El-Sisi A, Keshk AE, Torkey FA (2013) Cloud task scheduling based on ant colony optimization. In: 2013 8th International Conference on Computer Engineering & Systems (ICCES). IEEE, pp 64–69

  20. Vijindra R (2012) Energy efficient scheduling framework for cloud computing using ranking algorithm. Int J Sci Eng Res 3(10)

  21. Sharma S, Pandey HM (2016) Genetic algorithm, particle swarm optimization and harmony search: a quick comparison. In: 2016 6th International Conference on Cloud System and Big Data Engineering (Confluence). IEEE, pp 40–44

  22. Gulati A, Chopra RK (2013) Dynamic round robin for load balancing in a cloud computing. IJCSMC 2(6):274–278

    Google Scholar 

  23. Sethi S, Sahu A, Jena SK (2012) Efficient load balancing in cloud computing using fuzzy logic. IOSR J Eng 2(7):65–71

    Article  Google Scholar 

  24. Babukarthik RG, Raju R, Dhavachelvan P (2013) Hybrid algorithm for job scheduling: combining the benefits of ACO and Cuckoo search. In: Meghanathan N, Nagamalai D, Chaki N (eds) Advances in computing and information technology. Advances in intelligent systems and computing, vol 177. Springer, Berlin, Heidelberg

    Google Scholar 

  25. Glover F (1997) Tabu search and adaptive memory programming — advances, applications and challenges. In: Barr RS, Helgason RV, Kennington JL (eds) Interfaces in computer science and operations research. Operations research/computer science interfaces series, vol 7. Springer, Boston, MA

    Google Scholar 

  26. Adamuthe AC, Bichkar RS (2012) Tabu search for solving personnel scheduling problem. In: 2012 International Conference on Communication, Information & Computing Technology (ICCICT). IEEE, pp 1–6

  27. Zou D, Gao L, Li S, Wu J, Wang X (2010) A novel global harmony search algorithm for task assignment problem. J Syst Softw 83(10):1678–1688

    Article  Google Scholar 

  28. Wang X, Gao XZ, Zenger K (2015) The overview of Harmony search. In: An introduction to Harmony search optimization method. Springer, Cham, pp 5–11

    Google Scholar 

  29. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579

    MathSciNet  MATH  Google Scholar 

  30. Mohialdeen IA (2013) Comparative study of scheduling algorithms in cloud computing environment. J Comput Sci 9(2):252–263

    Article  Google Scholar 

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Correspondence to Hadeel Alazzam.

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Alazzam, H., Alhenawi, E. & Al-Sayyed, R. A hybrid job scheduling algorithm based on Tabu and Harmony search algorithms. J Supercomput 75, 7994–8011 (2019). https://doi.org/10.1007/s11227-019-02936-0

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