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PSO-based novel resource scheduling technique to improve QoS parameters in cloud computing

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Abstract

Cloud computing provides infinite resources and various services for the execution of variety of applications to end users, but still it has various challenges that need to be addressed. Objective of cloud users is to select the optimal resource that meets the demand of end users in reasonable cost and time, but sometimes users pay more for short time. Most of the proposed state-of-the-art algorithms try to optimize only one parameter at a time. Therefore, a novel compromise solution is needed to make the balance between conflicting objectives. The main goal of this research paper is to design and develop a task processing framework that has the decision-making capability to select the optimal resource at runtime to process the applications (diverse and complex nature) at virtual machines using modified particle swarm optimization (PSO) algorithm within a user-defined deadline. Proposed algorithm gives non-dominance set of optimal solutions and improves various influential parameters (time, cost, throughput, task acceptance ratio) by series of experiments over various synthetic datasets using Cloudsim tool. Computational results show that proposed algorithm well and substantially outperforms the baseline heuristic and meta-heuristic such as PSO, adaptive PSO, artificial bee colony, BAT algorithm, and improved min–min load-balancing algorithm.

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References

  1. Kumar M, Dubey K, Sharma SC (2018) Elastic and flexible deadline constraint load Balancing algorithm for Cloud Computing. Proc Comput Sci 125:717–724

    Google Scholar 

  2. Zhao J et al (2006) A heuristic clustering-based task deployment approach for load balancing using Bayes theorem in cloud environment. IEEE Trans Parallel Distrib Syst 27(2):305–316

    Google Scholar 

  3. Kalra M, Singh S (2015) A review of metaheuristic scheduling techniques in cloud computing. Egypt Inf J 16(3):275–295

    Google Scholar 

  4. Foster I et al (2008) Cloud computing and grid computing 360-degree compared. In: Grid computing environments workshop, pp 1–10

  5. Pande S et al (2010) Scheduling and management of data intensive application workflows in grid and cloud computing environment. University of Melbourne, Australia

    Google Scholar 

  6. Liu K et al (2010) A compromised time-cost scheduling algorithm in SwinDeW-C for instance-intensive cost-constrained workflows on cloud computing platform. Int J High Perform Comput Appl 24(4):445–456

    Google Scholar 

  7. Chen H, Wang F, Helian N, Akanmu G (2013) User priority guided min-min scheduling algorithm for cloud computing. In: National conference on parallel computing technologies (PARCOMPTECH), Bangalore, India, pp 1–8

  8. Mireslami S et al (2017) Simultaneous cost and QoS optimization for cloud resource allocation. IEEE Trans Netw Serv Manag 14(3):676–689

    Google Scholar 

  9. Xin Y et al (2017) A load balance oriented cost efficient scheduling method for parallel tasks. J Netw Comput Appl 81:37–46

    Google Scholar 

  10. Mashayekhy L et al (2016) An online mechanism for resource allocation and pricing in clouds. IEEE Trans Comput 65(4):1172–1184

    MathSciNet  MATH  Google Scholar 

  11. Arani M et al (2016) An autonomic approach for resource provisioning of cloud services. Clust Comput 19(3):1017–1036

    MathSciNet  Google Scholar 

  12. Pławiak P, Rzecki K (2015) Approximation of phenol concentration using computational intelligence methods based on signals from the metal-oxide sensor array. IEEE Sens J 15(3):1770–1783

    Google Scholar 

  13. Pławiak P et al (2016) Hand body language gesture recognition based on signals from specialized glove and machine learning algorithms. IEEE Trans Ind Inf 12(3):1104–1113

    Google Scholar 

  14. Pławiak P, Maziarz W (2014) Classification of tea specimens using novel hybrid artificial intelligence methods. Sens Actuators B Chem 192:117–125

    Google Scholar 

  15. Pławiak P (2018) Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm Evolut Comput 39:192–208

    Google Scholar 

  16. Pławiak P (2018) Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system. Expert Syst Appl 92:334–349

    Google Scholar 

  17. Książek W et al (2019) A novel machine learning approach for early detection of hepatocellular carcinoma patients. Cognit Syst Res 54:116–127

    Google Scholar 

  18. Meena J, Kumar M, Vardhan M (2016) Cost effective genetic algorithm for workflow scheduling in cloud under deadline constraint. IEEE Access 4:5065–5082

    Google Scholar 

  19. Pacini E et al (2015) Balancing throughput and response time in online scientific clouds via ant colony optimization (SP2013/2013/00006). Adv Eng Softw 84:31–47

    Google Scholar 

  20. Babu D, Venkata P (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput 13(5):2292–2303

    Google Scholar 

  21. Adhikari M et al (2019) Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud. J Netw Comput Appl 128:64–77

    Google Scholar 

  22. Ramezani F, Khadeer Hussain F (2013) Task-based system load balancing in cloud computing using particle swarm optimization. Int J Parallel Prog 42(5):739–754

    Google Scholar 

  23. Somasundaram TS, Govindarajan K (2014) CLOUDRB: a framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud. Future Gener Comput Syst 34:47–65

    Google Scholar 

  24. Pandey S et al (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: 2010 24th IEEE international conference on advanced information networking and applications (AINA), IEEE

  25. Verma Amandeep, Kaushal Sakshi (2017) A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. Parallel Comput 62:1–19

    MathSciNet  Google Scholar 

  26. Rodriguez MA, Buyya R (2014) Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans Cloud Comput 2(2):222–235

    Google Scholar 

  27. Netjinda N, Sirinaovakul B, Achalakul T (2014) Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization. J Supercomput 68(3):1579–1603

    Google Scholar 

  28. Gill S et al (2018) BULLET: particle swarm optimization based scheduling technique for provisioned cloud resources. J Netw Syst Manag 26(2):361–400

    Google Scholar 

  29. Adhikari M, Srirama S (2019) Multi-objective accelerated particle swarm optimization with a container-based scheduling for Internet-of-Things in cloud environment. J Netw Comput Appl 137:35–61

    Google Scholar 

  30. Kumar M, Sharma SC (2016) Priority aware longest job first (PA-LJF) algorithm for utilization of the resource in cloud environment. In: INDIACom, pp 415–420

  31. Kumar M, Sharma SC (2017) Dynamic load balancing algorithm for balancing the workload among virtual machine in cloud computing. In: 7th international conference on advances in computing and communications, ICACC-2017, 22-24 August 2017, Cochin, India, pp 322–329

  32. Kumar M, Sharma SC (2017) Dynamic load balancing algorithm to minimize the makespan time and utilize the resources effectively in cloud environment. Int J Comput Appl. https://doi.org/10.1080/1206212X.2017.1404823

    Article  Google Scholar 

  33. Kumar M, Sharma SC (2018) Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Comput Electr Eng 69:395–411

    Google Scholar 

  34. Tsai C-W, Rodrigues JJ (2014) Metaheuristic scheduling for cloud: a survey. IEEE Syst J 8:279–291

    Google Scholar 

  35. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: International conference on neural networks, pp 1942–1948

  36. Shelokar P, Siarry P, Jayaraman VK, Kulkarni BD (2007) Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Appl Math Comput 188:129–142

    MathSciNet  MATH  Google Scholar 

  37. Islam J et al (2017) A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO. Appl Soft Comput 59:182–196

    Google Scholar 

  38. Liang JJ, Qin AK, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal function. IEEE Trans Evol Comput 10(3):281–295

    Google Scholar 

  39. Wang Y, Li B, Weise T, Wang J, Yuan B, Tian Q (2011) Self-adaptive learning based particle swarm optimization. Inf Sci 181(20):4515–4538

    MathSciNet  MATH  Google Scholar 

  40. Kumar N, Vidyarthi D (2016) A model for resource-constrained project scheduling using adaptive PSO. Soft Comput 20(4):1565–1580

    Google Scholar 

  41. Xu Gang (2013) An adaptive parameter tuning of particle swarm optimization algorithm. Appl Math Comput 219(9):4560–4569

    MathSciNet  MATH  Google Scholar 

  42. Xu X et al (2014) EnReal: an energy-aware resource allocation method for scientific workflow executions in cloud environment. IEEE Trans Cloud Comput 4(2):166–179

    Google Scholar 

  43. Sindhu HS (2014) Comparative analysis of scheduling algorithms of Cloudsim in cloud computing. Int J Comput Appl 97(16):8887

    Google Scholar 

  44. Mashayekhya L, Grosu D (2016) An online mechanism for resource allocation and pricing in clouds. IEEE Trans Comput 65(4):1–13

    MathSciNet  Google Scholar 

  45. Wang H et al (2015) Enabling customer-provided resources for cloud computing: potentials, challenges, and implementation. IEEE Trans Parallel Distrib Syst 26(7):1874–1886

    Google Scholar 

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Authors and Affiliations

  1. SIT Sitamarhi, Sitamarhi, India

    Mohit Kumar

  2. IIT Roorkee, Roorkee, India

    S. C. Sharma

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  1. Mohit Kumar
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  2. S. C. Sharma
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Correspondence to Mohit Kumar.

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Kumar, M., Sharma, S.C. PSO-based novel resource scheduling technique to improve QoS parameters in cloud computing. Neural Comput & Applic 32, 12103–12126 (2020). https://doi.org/10.1007/s00521-019-04266-x

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  • DOI: https://doi.org/10.1007/s00521-019-04266-x

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