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Priority-based task scheduling method over cloudlet using a swarm intelligence algorithm

  • Sara Tabagchi Milan
  • Lila Rajabion
  • Aso Darwesh
  • Mehdi HosseinzadehEmail author
  • Nima Jafari Navimipour
Article
  • 37 Downloads

Abstract

In a cloud environment, scheduling problem as an NP-complete problem can be solved using various metaheuristic algorithms. The metaheuristic algorithms are very popular for scheduling tasks because of their effectiveness. A bacterial foraging is a swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. This paper proposes a task scheduling algorithm based on bacterial foraging optimization to reduce the idle time of virtual machines whereas the load balancing and reducing of runtime have occurred. The Cloudsim toolkit has assessed the performance of the proposed method in comparison with some scheduling algorithms. According to the obtained results, the makespan and energy consumption were reduced by using the proposed algorithm.

Keywords

Cloud computing Scheduling algorithms Energy Swarm intelligence algorithm 

Notes

References

  1. 1.
    Azhir, E., et al.: Query optimization mechanisms in the cloud environments: a systematic study. Int. J. Commun Syst. 32(8), e3940 (2019)Google Scholar
  2. 2.
    Naseri, A., Jafari Navimipour, N.: A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm. J. Ambient Intell. Humaniz. Comput. 10(5), 1851–1864 (2019)Google Scholar
  3. 3.
    Al Ridhawi, I., et al.: A continuous diversified vehicular cloud service availability framework for smart cities. Comput. Netw. 145, 207–218 (2018)Google Scholar
  4. 4.
    Al Ridhawi, I., et al.: A collaborative mobile edge computing and user solution for service composition in 5G systems. Trans. Emerg. Telecommun. Technol. 29(11), e3446 (2018)Google Scholar
  5. 5.
    Ebadi, Y., Jafari Navimipour, N.: An energy-aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm. Concurr. Comput. 31(1), e4757 (2019)Google Scholar
  6. 6.
    Azad, P., Navimipour, N.J.: An energy-aware task scheduling in the cloud computing using a hybrid cultural and ant colony optimization algorithm. Int. J. Cloud Appl. Comput. (IJCAC) 7(4), 20–40 (2017)Google Scholar
  7. 7.
    Garg, R., Mittal, M., Son, L.H.: Reliability and energy efficient workflow scheduling in cloud environment. Clust. Comput. (2019) Google Scholar
  8. 8.
    Shabestari, F., et al.: A taxonomy of software-based and hardware-based approaches for energy efficiency management in the Hadoop. J. Netw. Comput. Appl. 126, 162–177 (2019)Google Scholar
  9. 9.
    Mirzapour, F., et al.: A new prediction model of battery and wind-solar output in hybrid power system. J. Ambient Intell. Humaniz. Comput. 10(1), 77–87 (2019)Google Scholar
  10. 10.
    Lin, W., et al.: A heuristic task scheduling algorithm based on server power efficiency model in cloud environments. Sustain. Comput. 20, 56–65 (2017)Google Scholar
  11. 11.
    Rekha, P.M., Dakshayini, M.: Efficient task allocation approach using genetic algorithm for cloud environment. Clust. Comput. (2019)Google Scholar
  12. 12.
    Beloglazov, A., et al.: Chapter 3—a taxonomy and survey of energy-efficient data centers and cloud computing systems. In: Zelkowitz, M.V. (ed.) Advances in Computers, pp. 47–111. Elsevier, Amsterdam (2011)Google Scholar
  13. 13.
    Aghajani, G., Ghadimi, N.: Multi-objective energy management in a micro-grid. Energy Rep. 4, 218–225 (2018)Google Scholar
  14. 14.
    Abedinia, O., Amjady, N., Ghadimi, N.: Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput. Intell. 34(1), 241–260 (2018)MathSciNetGoogle Scholar
  15. 15.
    Nouri, A., et al.: Optimal performance of fuel cell-CHP-battery based micro-grid under real-time energy management: an epsilon constraint method and fuzzy satisfying approach. Energy 159, 121–133 (2018)Google Scholar
  16. 16.
    Ahmadian, I., Abedinia, O., Ghadimi, N.: Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization. Front. Energy 8(4), 412–425 (2014)Google Scholar
  17. 17.
    Hamian, M., et al.: A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm. Eng. Appl. Artif. Intell. 72, 203–212 (2018)Google Scholar
  18. 18.
    Keshanchi, B., Navimipour, N.J.: Priority-based task scheduling in the cloud systems using a memetic algorithm. J. Circuits Syst. Comput. 25(10), 1650119 (2016)Google Scholar
  19. 19.
    Ashouraie, M., Jafari Navimipour, N.: Priority-based task scheduling on heterogeneous resources in the Expert Cloud. Kybernetes 44(10), 1455–1471 (2015)Google Scholar
  20. 20.
    Ghadimi, N., Afkousi-Paqaleh, M., Nouri, A.: PSO based fuzzy stochastic long-term model for deployment of distributed energy resources in distribution systems with several objectives. IEEE Syst. J. 7(4), 786–796 (2013)Google Scholar
  21. 21.
    Manafi, H., et al.: Optimal placement of distributed generations in radial distribution systems using various PSO and DE algorithms. Elektron. Elektrotech. 19(10), 53–57 (2013)Google Scholar
  22. 22.
    Ghadimi, N.: Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Complexity 21(1), 78–93 (2015)MathSciNetGoogle Scholar
  23. 23.
    Jalili, A., Ghadimi, N.: Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market. Complexity 21(S1), 90–98 (2016)MathSciNetGoogle Scholar
  24. 24.
    Ghadimi, N., Afkousi-Paqaleh, A., Emamhosseini, A.: A PSO-based fuzzy long-term multi-objective optimization approach for placement and parameter setting of UPFC. Arab. J. Sci. Eng. 39(4), 2953–2963 (2014)zbMATHGoogle Scholar
  25. 25.
    Morsali, R., et al.: Solving a novel multiobjective placement problem of recloser and distributed generation sources in simultaneous mode by improved harmony search algorithm. Complexity 21(1), 328–339 (2015)MathSciNetGoogle Scholar
  26. 26.
    Mir, M., et al.: Applying ANFIS-PSO algorithm as a novel accurate approach for prediction of gas density. Pet. Sci. Technol. 36(12), 820–826 (2018)Google Scholar
  27. 27.
    Razavi, R., et al.: Utilization of LSSVM algorithm for estimating synthetic natural gas density. Pet. Sci. Technol. 36(11), 807–812 (2018)Google Scholar
  28. 28.
    Calheiros, R.N., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exper. 41(1), 23–50 (2011)MathSciNetGoogle Scholar
  29. 29.
    Gai, K., et al.: Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. J. Netw. Comput. Appl. 59, 46–54 (2016)Google Scholar
  30. 30.
    Chana, I.: Bacterial foraging based hyper-heuristic for resource scheduling in grid computing. Future Gener. Comput. Syst. 29(3), 751–762 (2013)Google Scholar
  31. 31.
    Abdullahi, M., Ngadi, M.A., Abdulhamid, S.I.M.: Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Gener. Comput. Syst. 56, 640–650 (2016)Google Scholar
  32. 32.
    Changtian, Y., Jiong, Y.: Energy-aware genetic algorithms for task scheduling in cloud computing. In: Seventh ChinaGrid Annual Conference (2012)Google Scholar
  33. 33.
    Dai, Y., Lou, Y., Lu, X.: A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-QoS constraints in cloud computing. In: 7th International conference on intelligent human-machine systems and cybernetics (2015)Google Scholar
  34. 34.
    Alkayal, E.S., Jennings, N.R., Abulkhair, M.F.: Efficient task scheduling multi-objective particle swarm optimization in cloud computing. In: IEEE 41st conference on local computer networks workshops (LCN workshops), pp. 17–24. (2016).Google Scholar
  35. 35.
    Singh, S., Chana, I.: Q-aware: quality of service based cloud resource provisioning. Comput. Electr. Eng. 47, 138–160 (2015)Google Scholar
  36. 36.
  37. 37.
    Lu, Y., Sun, N.: An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment. Clust. Comput. (2017)Google Scholar
  38. 38.
    Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)Google Scholar
  39. 39.
    Mustafa, S., et al.: SLA-aware energy efficient resource management for cloud environments. IEEE Access 6, 15004–15020 (2018)Google Scholar
  40. 40.
    Mishra, S.K., et al.: Energy-efficient VM-placement in cloud data center. Sustain. Comput. 20, 48–55 (2018)Google Scholar
  41. 41.
    Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurr. Comput. 24(13), 1397–1420 (2012)Google Scholar
  42. 42.
    Zhong, Z., et al.: Virtual machine-based task scheduling algorithm in a cloud computing environment. Tsinghua Sci. Technol. 21(6), 660–667 (2016)zbMATHGoogle Scholar
  43. 43.
    Braun, T.D., et al.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61(6), 810–837 (2001)zbMATHGoogle Scholar
  44. 44.
    Calheiros, R.N., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software 41(1), 23–50 (2011)MathSciNetGoogle Scholar
  45. 45.
    Muthulakshmi, B., Somasundaram, K.: A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment. Clust. Comput. (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, Tabriz BranchIslamic Azad UniversityTabrizIran
  2. 2.College of BusinessUniversity of South Florida Sarasota-ManateeFloridaUSA
  3. 3.Information Technology DepartmentUniversity of Human DevelopmentSulaymaniyahIraq
  4. 4.Health Management and Economics Research CenterIran University of Medical SciencesTehranIran
  5. 5.Computer ScienceUniversity of Human DevelopmentSulaymaniyahIraq
  6. 6.Young Researchers and Elite Club, Islamshahr BranchIslamic Azad UniversityIslamshahrIran

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