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Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing

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Abstract

In order to solve the problems of unbalanced load, slow convergence speed and low utilization of virtual machine resources existing in the previous task scheduling optimization strategies, this paper proposes a task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing. Firstly, based on the principle of cloud computing task scheduling, a scheduling model using improved ant colony algorithm is proposed to avoid the optimization strategy falling into local optimization. Then, task scheduling satisfaction function is constructed by combining the three objectives of the shortest waiting time, the degree of resource load balance and the cost of task completion to search the optimal solution of task scheduling. Finally, the reward and punishment coefficient is introduced to optimize the pheromone updating rules of ant colony algorithm, which speeds up the solution speed. Besides, we use dynamic update of volatility coefficient to optimize overall performance of this strategy, and introduce virtual machine load weight coefficient in the process of local pheromone updating, so as to ensure the load balance of virtual machine. The feasibility of our algorithm is analyzed and demonstrated by experiments with Cloudsim. The experimental results show that the proposed algorithm has the fastest convergence speed, the shortest completion time, the most balanced load and the highest utilization rate of virtual machine resources compared with other methods. Therefore, our proposed task scheduling optimization strategy has the best performance.

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References

  • Abd Elaziz M, Xiong S, Jayasena KPN et al (2019) Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution. Knowl-Based Syst 169(04):39–52

    Article  Google Scholar 

  • Boveiri HR, Khayami R, Elhoseny M et al (2019) An efficient Swarm-Intelligence approach for task scheduling in cloud- based internet of things applications. J Ambient Intell Humaniz Comput 10(9):3469–3479

    Article  Google Scholar 

  • Chen W, Wang D, Li K (2019) Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Trans Serv Comput 12(5):726–738

    Article  Google Scholar 

  • Domanal SG, Guddeti RMR, Buyya R (2020) A hybrid bio-inspired algorithm for scheduling and resource management in cloud environment. IEEE Trans Serv Comput 13(1):3–15

    Article  Google Scholar 

  • Garg S, Chaurasia PK (2019) Application of genetic algorithms task scheduling in cloud computing. Int J Comput Sci Eng 7(6):782–787

    Google Scholar 

  • Gong X, Liu Y, Lohse N et al (2019) Energy- and labor-aware production scheduling for industrial demand response using adaptive multiobjective memetic algorithm. IEEE Trans Industr Inf 15(2):942–953

    Article  Google Scholar 

  • Guo S, Liu J, Yang Y et al (2019) Energy-efficient dynamic computation offloading and cooperative task scheduling in mobile cloud computing. IEEE Trans Mob Comput 18(2):319–333

    Article  Google Scholar 

  • Haidri RA, Katti CP, Saxena PC (2019) Cost-effective deadline-aware stochastic scheduling strategy for workflow applications on virtual machines in cloud computing. Concurr Comp-Pract Exper 31(7):1–24

    Google Scholar 

  • Hung PP, Alam G, Hai N et al (2019) A dynamic scheduling method for collaborated cloud with thick clients. Int Arab J Inf Technol 16(4):633–643

    Google Scholar 

  • Jain R (2020) EACO: an enhanced ant colony optimization algorithm for task scheduling in cloud computing. Int J Secur Appl 13(4):91–100

    Google Scholar 

  • Karthikeyan T, Vinothkumar A, Ramasamy P (2019) Priority based scheduling in cloud computing based on task—aware technique. J Comput Theor Nanosci 16(5):1942–1946

    Article  Google Scholar 

  • Kaur A, Sood SK (2020) Cloud-Fog based framework for drought prediction and forecasting using artificial neural network and genetic algorithm. J Exp Theor Artif Intell 32(2):273–289

    Article  Google Scholar 

  • Kaur A, Kaur B, Singh D (2019) Meta-heuristic based framework for workflow load balancing in cloud environment. Int J Inf Technol 11(1):119–125

    Google Scholar 

  • Khan WU, Ye Z, Altaf F et al (2019) A novel application of fireworks heuristic paradigms for reliable treatment of nonlinear active noise control. Appl Acoustics 146(MAR):246–260

    Article  Google Scholar 

  • Mansouri N, Zade BMH, Javidi MM (2019) Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory. Comput Ind Eng 130(04):597–633

    Article  Google Scholar 

  • Marahatta A, Wang Y, Zhang F et al (2019) Energy-aware fault-tolerant dynamic task scheduling scheme for virtualized cloud data centers. Mobile Netw Appl 24(3):1063–1077

    Article  Google Scholar 

  • Matos JGD, Marques CKDM, Liberalino CHP (2019) Genetic and static algorithm for task scheduling in cloud computing. Int J Cloud Comput 8(1):1–19

    Article  Google Scholar 

  • Meshkati J, Safi-Esfahani F (2019) Energy-aware resource utilization based on particle swarm optimization and artificial bee colony algorithms in cloud computing. J Supercomput 75(5):2455–2496

    Article  Google Scholar 

  • Nayak SC, Tripathy C (2019) An improved task scheduling mechanism using multi-criteria decision making in cloud computing. Int J Inf Technol Web Eng 14(2):92–117

    Article  Google Scholar 

  • Neelakanteswara P, Babu PS (2019) Efficient trust management technique using neural network in cloud computing. J Comput Netw Wirel Mobile Commun 9(1):29–40

    Google Scholar 

  • Ray K, Sharma T K, Rawat S, et al (2019). [Advances in Intelligent Systems and Computing] Soft computing: theories and applications volume 742 (Proceedings of SoCTA 2017) || a PSO algorithm-based task scheduling in cloud computing 10(27): 295–301

  • Reddy GN, Kumar SP (2019) Regressive whale optimization for workflow scheduling in cloud computing. Int J Comput Intell Appl 18(04):147–156

    Google Scholar 

  • Selvakumar A, Gunasekaran G (2019) A novel approach of load balancing and task scheduling using ant colony optimization algorithm. Int J Softw Innov 7(2):9–20

    Article  Google Scholar 

  • Shao X, Xie Z (2019) A scheduling algorithm for applications in a cloud computing system with communication changes. Expert Syst 36(2):1–18

    Article  MathSciNet  Google Scholar 

  • Sreenu K, Malempati S (2019) MFGMTS: epsilon constraint-based modified fractional grey wolf optimizer for multi-objective task scheduling in cloud computing. IETE J Res 65(2):201–215

    Article  Google Scholar 

  • Vila S, Guirado F, Lerida JL et al (2019) Energy-saving scheduling on laaS HPC cloud environments based on a multi-objective genetic algorithm. J Supercomput 75(3):1483–1495

    Article  Google Scholar 

  • Wakil K, Badfar A, Dehghani P et al (2019) A fuzzy logic-based method for solving the scheduling problem in the cloud environments using a non-dominated sorted algorithm. Concurr Pract Exper 31(17):1–12

    Google Scholar 

  • Wang H, Xiao G, Wei Z et al (2019) Network optimisation for improving security and safety level of dangerous goods transportation based on cloud computing. Int J Inf Comput Secur 11(2):160–177

    Google Scholar 

  • Wu L, Tian X, Wang H et al (2019) Improved ant colony optimization algorithm and its application to solve pipe routing design. Assembly Autom 39(1):45–57

    Article  Google Scholar 

  • Xie Y, Zhu Y, Wang Y et al (2019) A novel directional and non-local-convergent particle swarm optimization based workflow scheduling in cloud-edge environment. Fut Gener Comput Syst 97(08):361–378

    Article  Google Scholar 

  • Yuan H, Bi J, Zhou MC (2019) Spatial task scheduling for cost minimization in distributed green cloud data centers. IEEE Trans Autom Sci Eng 16(2):729–740

    Article  Google Scholar 

  • Zhang Y (2019) Classified scheduling algorithm of big data under cloud computing. Int J Comput Appl 41(3–4):262–267

    Google Scholar 

  • Zhou Z, Xie H, Li F (2019) A novel task scheduling algorithm integrated with priority and greedy strategy in cloud computing. J Intell Fuzzy Syst 37(4):1–9

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Young Backbone Teachers Funding Project of Henan Colleges and Universities (No. 2017GGJS263)

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  1. Shangqiu Polytechnic, Shangqiu, 476000, Henan, China

    Xianyong Wei

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  1. Xianyong Wei
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Wei, X. Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02614-7

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