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A quantum inspired hybrid SSA–GWO algorithm for SLA based task scheduling to improve QoS parameter in cloud computing

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Abstract

Software as a service (SaaS) provider hires resources from an Infrastructure as a Service (IaaS) provider and provides these sharable resources to user's applications on lease. However, it is becoming a more challenging issue for SaaS providers to meet user's Quality of Service (QoS) Parameter and maximize profit from cloud infrastructure. This proposed work satisfies both the user and the service provider by fulfilling service level agreement (SLA), user's QoS requirement, and increasing profit with efficient resources utilization. This paper proposes an Improved Quantum Salp Swarm Algorithm (IQSSA), which improves the Salp Swarm algorithm by incorporating the principles of Quantum computing to increase the convergence rate. Further, Quantum-inspired Salp Swarm Grey Wolf Algorithm (QSSGWA) embeds SSA with Grey Wolf Optimizer (GWO) to improve the global optimum solution, and quantum operator is used to initializing population. Proposed algorithms execute tasks under the user-defined deadline and budget constraints. Furthermore, the penalty cost is formulated and applied in the case of a deadline violation. IQSSA and QSSGWA are tested on 19 global benchmark functions, and results prove their superior performance compared to SSA, GWO, BAT, and Particle Swarm Optimization (PSO) algorithm. Furthermore, these algorithms are simulated on CloudSim, and performance matrices such as service provider's profit, makespan, SLA violation rate, task rejection rate, throughput, resource utilization, and response time are compared. The comparison analysis demonstrates that the proposed algorithms offer better performance and more efficient scheduling than existing metaheuristics. Furthermore, simulation results clearly show that QSSGWA gives the best results for all performance matrices. This proposed approach can be applied in many scientific domains, where distributed processing of data or large scale data analysis is required such as distributed and federated machine learning, serverless computing, medical applications, etc.

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References

  1. Gill, S.S., Buyya, R., Chana, I., Singh, M., Abraham, A.: BULLET: particle swarm optimization based scheduling technique for provisioned cloud resources. J. Netw. Syst. Manag. 26(2), 361–400 (2018)

    Article  Google Scholar 

  2. Yeo, C.S., Buyya, R.: Service level agreement based allocation of cluster resources: handling penalty to enhance utility. In: 2005 IEEE International Conference on Cluster Computing, 2005, pp. 1–10. IEEE (2005)

  3. Emeakaroha, V.C., Netto, M.A.S., Calheiros, R.N., Brandic, I., Buyya, R., De Rose, C.A.F.: Towards autonomic detection of SLA violations in Cloud infrastructures. Future Gener. Comput. Syst. 28(7), 1017–1029 (2012)

    Article  Google Scholar 

  4. Ullman, J.D.: NP-complete scheduling problems. J. Comput. Syst. Sci. 10(3), 384–393 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chakravarthi, K.K., Shyamala, L., Vaidehi, V.: Cost-effective workflow scheduling approach on cloud under deadline constraint using firefly algorithm. Appl. Intell. 51(3), 1629–1644 (2021)

    Article  Google Scholar 

  6. Rizvi, N., Dharavath, R., Edla, D.R.: Cost and makespan aware workflow scheduling in IaaS clouds using hybrid spider monkey optimization. Simul. Model. Pract. Theory 110, 102328 (2021)

    Article  Google Scholar 

  7. Oprescu, A.-M., Kielmann, T.: Bag-of-tasks scheduling under budget constraints. In: 2010 IEEE Second International Conference on Cloud Computing Technology and Science, 2010, pp. 351–359. IEEE (2010)

  8. Zeng, L., Veeravalli, B., Li, X.: SABA: a security-aware and budget-aware workflow scheduling strategy in clouds. J. Parallel Distrib. Comput. 75, 141–151 (2015)

    Article  Google Scholar 

  9. Canon, L.-C., Chang, A.K.W., Robert, Y., Vivien, F.: Scheduling independent stochastic tasks under deadline and budget constraints. Int. J. High Perform. Comput. Appl. 34(2), 246–264 (2020)

    Article  Google Scholar 

  10. Kaur, T., Chana, I.: GreenSched: an intelligent energy aware scheduling for deadline-and-budget constrained cloud tasks. Simul. Model. Pract. Theory 82, 55–83 (2018)

    Article  Google Scholar 

  11. Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017)

    Article  Google Scholar 

  12. Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M., Heidari, A.A.: Salp swarm algorithm: theory, literature review, and application in extreme learning machines. In: Nature Inspired Optimizers, pp. 185–199. Springer, Cham (2020)

  13. Jain, R., Sharma, N.: A QoS aware binary salp swarm algorithm for effective task scheduling in cloud computing. In: Progress in Advanced Computing and Intelligent Engineering, pp. 462–473. Springer, Singapore (2021)

  14. Abualigah, L., Shehab, M., Alshinwan, M., Alabool, H.: Salp swarm algorithm: a comprehensive survey. Neural Comput. Appl. 32, 1–21 (2019)

  15. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  16. Sun, J., Xu, W., Feng, B.: A global search strategy of quantum-behaved particle swarm optimization. In: IEEE Conference on Cybernetics and Intelligent Systems, 2004, vol. 1, pp. 111–116. IEEE (2004)

  17. Jia, P., Duan, S., Yan, J.: An enhanced quantum-behaved particle swarm optimization based on a novel computing way of local attractor. Information 6(4), 633–649 (2015)

    Article  Google Scholar 

  18. Han, K.-H., Kim, J.-H.: Genetic quantum algorithm and its application to combinatorial optimization problem. In: Proceedings of the 2000 Congress on Evolutionary Computation CEC00 (Cat. No. 00TH8512), vol. 2, pp. 1354–1360. IEEE (2000)

  19. Chen, R., Dong, C., Ye, Y., Chen, Z., Liu, Y.: QSSA: quantum evolutionary salp swarm algorithm for mechanical design. IEEE Access 7, 145582–145595 (2019)

    Article  Google Scholar 

  20. Sayed, G.I., Khoriba, G., Haggag, M.H.: Hybrid quantum salp swarm algorithm for contrast enhancement of natural images. Int. J. Intell. Eng. Syst. 12(6), 225–235 (2019)

    Google Scholar 

  21. Tian, F., Wei, H., Li, X., Lv, M., Wang, P.: An improved salp optimization algorithm inspired by quantum computing. J. Phys. Conf. Ser. 1570(1), 012016 (2020)

    Article  Google Scholar 

  22. Vijay, R.K., Nanda, S.J.: A Quantum Grey Wolf Optimizer based declustering model for analysis of earthquake catalogs in an ergodic framework. J. Comput. Sci. 36, 101019 (2019)

    Article  Google Scholar 

  23. Thakur, A.S., Biswas, T., Kuila, P.: Binary quantum-inspired gravitational search algorithm-based multi-criteria scheduling for multi-processor computing systems. J. Supercomput. 77(1), 796–817 (2021)

    Article  Google Scholar 

  24. Ross, O.H.M.: A review of quantum-inspired metaheuristics: going from classical computers to real quantum computers. IEEE Access 8, 814–838 (2019)

    Article  Google Scholar 

  25. Panda, S.K., Jana, P.K.: SLA-based task scheduling algorithms for heterogeneous multi-cloud environment. J. Supercomput. 73(6), 2730–2762 (2017)

    Article  Google Scholar 

  26. Barthwal, V., Rauthan, M.M.S.: AntPu: a meta-heuristic approach for energy-efficient and SLA aware management of virtual machines in cloud computing. Memet. Comput. 13(1), 91–110 (2021)

    Article  Google Scholar 

  27. Alworafi, M.A., Dhari, A., El-Booz, S.A., Mallappa, S.: Budget-aware task scheduling technique for efficient management of cloud resources. Int. J. High Perform. Comput. Netw. 14(4), 453–465 (2019)

    Article  Google Scholar 

  28. Khelifa, A., Hamrouni, T., Mokadem, R., Charrada, F.B.: Combining task scheduling and data replication for SLA compliance and enhancement of provider profit in clouds. Appl. Intell. 51, 1–23 (2021)

    Article  Google Scholar 

  29. Kumar, A., Bawa, S.: A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services. Soft Comput. 24(6), 3909–3922 (2020)

    Article  Google Scholar 

  30. Zuo, X., Zhang, G., Tan, W.: Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Trans. Autom. Sci. Eng. 11(2), 564–573 (2013)

    Article  Google Scholar 

  31. Visheratin, A.A., Melnik, M., Nasonov, D.: Workflow scheduling algorithms for hard-deadline constrained cloud environments. Procedia Comput. Sci. 80, 2098–2106 (2016)

    Article  Google Scholar 

  32. Garg, N., Singh, D., Goraya, M.S.: Deadline aware energy-efficient task scheduling model for a virtualized server. SN Comput. Sci. 2(3), 1–15 (2021)

    Article  Google Scholar 

  33. Kumar, M., Sharma, S.C.: PSO-COGENT: cost and energy efficient scheduling in cloud environment with deadline constraint. Sustain. Comput. Inform. Syst. 19, 147–164 (2018)

    Google Scholar 

  34. Chen, Z.-G., Du, K.-J., Zhan, Z.-H., Zhang, J.: Deadline constrained cloud computing resources scheduling for cost optimization based on dynamic objective genetic algorithm. In: 2015 IEEE Congress on Evolutionary Computation (CEC), 2015, pp. 708–714. IEEE (2015)

  35. 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 

  36. Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3, 2687–2699 (2015)

    Article  Google Scholar 

  37. Wu, Q., Ishikawa, F., Zhu, Q., Xia, Y., Wen, J.: Deadline-constrained cost optimization approaches for workflow scheduling in clouds. IEEE Trans. Parallel Distrib. Syst. 28(12), 3401–3412 (2017)

    Article  Google Scholar 

  38. Raju, I.R.K., Varma, P.S., Rama Sundari, M., Jose Moses, G.: Deadline aware two stage scheduling algorithm in cloud computing. Indian J. Sci. Technol. 9(4), 1–10 (2016)

    Google Scholar 

  39. Nayak, S.C., Parida, S., Tripathy, C., Pattnaik, P.K.: An enhanced deadline constraint based task scheduling mechanism for cloud environment. J. King Saud Univ. Comput. Inf. Sci. 34(2), 282–294 (2018)

  40. Hwang, E., Kim, K.H.: Minimizing cost of virtual machines for deadline-constrained MapReduce applications in the cloud. In: 2012 ACM/IEEE 13th International Conference on Grid Computing, 2012, pp. 130–138. IEEE (2012)

  41. He, X., et al.: A two-stage scheduling method for deadline-constrained task in cloud computing. Clust. Comput. 25, 1–17 (2022)

  42. Zhang, L., et al.: EM_WOA: a budget-constrained energy consumption optimization approach for workflow scheduling in clouds. Peer-to-Peer Netw. Appl. 15(2), 973–987 (2022)

    Article  Google Scholar 

  43. Li, H., et al.: Improved swarm search algorithm for scheduling budget-constrained workflows in the cloud. Soft Comput. 26(8), 3809–3824s (2022)

    Article  MathSciNet  Google Scholar 

  44. Chakravarthi, K.K., Shyamala, L., Vaidehi, V.: Budget aware scheduling algorithm for workflow applications in IaaS clouds. Clust. Comput. 23, 1–15 (2020)

  45. Qin, Y., Wang, H., Yi, S., Li, X., Zhai, L.: An energy-aware scheduling algorithm for budget-constrained scientific workflows based on multi-objective reinforcement learning. J. Supercomput. 76(1), 455–480 (2020)

    Article  Google Scholar 

  46. Verma, A., Kaushal, S.: Deadline and budget distribution based cost-time optimization workflow scheduling algorithm for cloud. In: IJCA Proceedings on International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT 2012), vol. 4, pp. 1–4. iRAFIT (7), 2012.

  47. Zhou, N., Lin, W., Feng, W., Shi, F., Pang, X.: Budget-deadline constrained approach for scientific workflows scheduling in a cloud environment. Clust. Comput. (2020). https://doi.org/10.1007/s10586-020-03176-1

  48. Arabnejad, H., Barbosa, J.G., Prodan, R.: Low-time complexity budget-deadline constrained workflow scheduling on heterogeneous resources. Future Gener. Comput. Syst. 55, 29–40 (2016)

    Article  Google Scholar 

  49. Sun, T., Xiao, C., Xu, X.: A scheduling algorithm using sub-deadline for workflow applications under budget and deadline constrained. Clust. Comput. 22(3), 5987–5996 (2019)

    Article  Google Scholar 

  50. Verma, A., Kaushal, S.: Bi-criteria priority based particle swarm optimization workflow scheduling algorithm for cloud. In: 2014 Recent Advances in Engineering and Computational Sciences (RAECS), 2014, pp. 1–6. IEEE (2014)

  51. Jing, W., Zhao, C., Miao, Q., Song, H., Chen, G.: QoS-DPSO: QoS-aware task scheduling for cloud computing system. J. Netw. Syst. Manag. 29(1), 1–29 (2021)

    Article  Google Scholar 

  52. Alworafi, M.A., Mallappa, S.: A collaboration of deadline and budget constraints for task scheduling in cloud computing. Clust. Comput. 23(2), 1073–1083 (2020)

    Article  Google Scholar 

  53. Amer, D.A., et al.: Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing. J. Supercomput. 78(2), 2793–2818 (2022)

    Article  Google Scholar 

  54. Amazon EC2 pricing [EB/OL]. http://aws.amazon.com/ec2/pricing

  55. NASA: The NASA Ames iPSC/860 Log. NASA Ames IPSC/860. https://www.cs.huji.ac.il/labs/parallel/workload/l_nasa_ipsc/ (2011). Accessed 22 May 2022

  56. Wu, L., Garg, S.K., Buyya, R.: SLA-based admission control for a Software-as-a-Service provider in Cloud computing environments. J. Comput. Syst. Sci. 78(5), 1280–1299 (2012)

    Article  Google Scholar 

  57. Calheiros, R.N., Ranjan, R., Beloglazov, A., Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software 41(1), 23–50 (2010)

    Google Scholar 

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  1. Department of Computer Science and Engineering, Banasthali Vidyapith, Niwai, India

    Richa Jain & Neelam Sharma

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  1. Richa Jain
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RJ conducted the experiments, performed the data analyses and wrote the manuscript; NS performed the analysis with constructive discussions.

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Correspondence to Richa Jain.

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Jain, R., Sharma, N. A quantum inspired hybrid SSA–GWO algorithm for SLA based task scheduling to improve QoS parameter in cloud computing. Cluster Comput 26, 3587–3610 (2023). https://doi.org/10.1007/s10586-022-03740-x

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