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Improving QoS of Cloudlet Scheduling via Effective Particle Swarm Model

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Machine Learning, Advances in Computing, Renewable Energy and Communication

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 768))

Abstract

Cloud computing (CC) is an emerging area that includes the provisioning of dynamic and virtualized resources in a pay-as-you-go manner. Much exploration is required to enhance the scalability, effectiveness, and equilibrium in load balancing for better scheduling. Various scheduling algorithms are proposed to meet the user’s requirements, but most of them failed to balance the load in critical resource demand hours. In this study, an effective particle swarm algorithm (EPSO) is addressed to solve the scheduling problem. For a faster discovery of resources, a reverse variation technique is employed in the proposed approach. The EPSO model is applied after readjustment and fine-tuning of its hyper-parameters to get precise and optimized results. This study provides a better quality of service (QoS) by optimizing resource utilization, service availability, and service-level agreement (SLA). Standard deviation (SD) is one of the critical statistical load distribution parameters computed to confirm the correct results. Results demonstrated in the form of graphs, tables, FCFS, RR, and SJF scheduling models confirm that EPSO provides a better outcome than the other state-of-the-art methods.

Supported by Graphic Era Deemed University, Dehradun.

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References

  1. Huang Q, Huang T (2010) An optimistic job scheduling strategy based on QoS for cloud computing. In: 2010 international conference on intelligent computing and integrated systems. IEEE

    Google Scholar 

  2. Zhan S, Huo H (2012) Improved PSO-based task scheduling algorithm in cloud computing. J Inf Comput Sci 9(13):3821–3829

    Google Scholar 

  3. Madni SHH et al (2017) Recent advancements in resource allocation techniques for cloud computing environment: a systematic review. Clust Comput 20 (3):2489–2533

    Google Scholar 

  4. Hassan R et al (2005) A comparison of particle swarm optimization and the genetic algorithm. In: 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference

    Google Scholar 

  5. Madni SHH et al (2016) An appraisal of meta-heuristic resource allocation techniques for IaaS cloud. Indian J Sci Technol 9(4):1–14

    Google Scholar 

  6. Ramezani F et al (2013) Task scheduling optimization in cloud computing applying multiobjective particle swarm optimization. In: International conference on service-oriented computing. Springer, Berlin, Heidelberg

    Google Scholar 

  7. Bhatt K, Bundele M (2013) Study and impact of CloudSim on the run of PSO in cloud environment. Int J Innov Eng Technol 2(4):254–262

    Google Scholar 

  8. Milani AS, Navimipour NJ (2016) Load balancing mechanisms and techniques in the cloud environments: systematic literature review and future trends. J Netw Comput Appl 71:86–98

    Google Scholar 

  9. Zuo X et al (2014) Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Trans Autom Sci Eng 11(2):564–573

    Google Scholar 

  10. Abdi S et al (2014) Task scheduling using modified PSO algorithm in cloud computing environment. In: International conference on machine learning, electrical and mechanical engineering

    Google Scholar 

  11. Almezeini N, Hafez A (2017) Task scheduling in cloud computing using lion optimization algorithm. Algorithms 5:7

    Google Scholar 

  12. Xu L et al (2014) An improved binary PSO-based task scheduling algorithm in green cloud computing. In: 9th international conference on communications and networking in China. IEEE

    Google Scholar 

  13. Madni SHH et al (2017) Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PloS one 12(5):e0176321

    Google Scholar 

  14. Shelokar PS et al (2007) Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Appl Math Comput 188(1):129–142

    MathSciNet  MATH  Google Scholar 

  15. Tareghian S, Bornaee Z (2015) A new approach for scheduling jobs in cloud computing environment. Fen Bilimleri Dergisi (CFD) 36(3)

    Google Scholar 

  16. Tawfeek MA (2013) Cloud task scheduling based on ant colony optimization. In: 2013 8th international conference on computer engineering and systems (ICCES). IEEE

    Google Scholar 

  17. Zhang L et al (2008) A task scheduling algorithm based on PSO for grid computing. Int J Comput Intell Res 4(1):37–43

    Google Scholar 

  18. Wang L et al (2008) Scientific cloud computing: early definition and experience. In: 2008 10th IEEE international conference on high performance computing and communications. IEEE

    Google Scholar 

  19. Calheiros RN et al (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50

    Google Scholar 

  20. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science. IEEE

    Google Scholar 

  21. Zhan Z, Liu X, Gong Y, Zhang J, Chung HS, Li Y (2015) Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput Surveys 47(4):33 (Article 63)

    Google Scholar 

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

    Article  Google Scholar 

  23. Feng M, Wang X, Zhang Y, Li J (2012) Multi-objective particle swarm optimization for reseource allocation in cloud computing. In: Proceedings of 2nd international conference on cloud computing and intelligent systems (CCIS), vol 3, pp 1161–1165

    Google Scholar 

  24. Guo L, Shao G, Zhao S (2012) Multi-objective task assignment in cloud computing by particle swarm optimization. In: Proceedings of 8th international conference on wireless communications, networking and mobile computing (WiCOM), pp 1–4

    Google Scholar 

  25. Alkayal ES, Jennings NR, Abulkhair MF (2016) Efficient task scheduling multi-objective particle swarm optimization in cloud computing. In: Proceedings of 41st IEEE conference on local computer networks workshops, pp 17–24

    Google Scholar 

  26. Leena VA, Beegom ASA, Rajasree MS (2016) Genetic algorithm based bi-objective task scheduling in hybrid cloud platform. Int J Comput Theory Eng 8(1):7–13

    Article  Google Scholar 

  27. Elhady GF, Tawfeek MA (2015) A comparative study into swarm intelligence algorithms for dynamic task scheduling in cloud computing. In: Proceedings of 7th IEEE International Conference on Intelligent Computing and Information Systems, pp 362–369

    Google Scholar 

  28. Beegom ASA, Rajasree MS (2019) Integer-pso: a discrete pso algorithm for task scheduling in cloud computing systems. Evol Intell 12(2):227–239

    Google Scholar 

  29. Wang X, Wang Y (2012) An energy and data locality aware bilevel multiobjective task scheduling model based on map reduce for cloud computing. In: Proceedings of IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, pp 648–655

    Google Scholar 

  30. Agrawal A, Tripathi S (2018) Particle swarm optimization with adaptive inertia weight based on cumulative binomial probability. Evol Intell 1–9

    Google Scholar 

  31. Shahid A, Qadri MY, Fleury M, Waris H, Ahmad A, Qadri NN (2018) Ac-dse: approximate computing for the design space exploration of reconfigurable mpsocs. J Circuits Syst Comput 27(9):25

    Article  Google Scholar 

  32. Aggarwal S et al (2020) Meta heuristic and evolutionary computation: algorithms and applications. Springer Nature, Berlin, p 949. https://doi.org/10.1007/978-981-15-7571-6. ISBN: 978-981-15-7571-6

  33. Yadav AK et al (2020) Soft computing in condition monitoring and diagnostics of electrical and mechanical systems. Springer Nature, Berlin, p 496. https://doi.org/10.1007/978-981-15-1532-3. ISBN: 978-981-15-1532-3

  34. Gopal et al (2021) Digital transformation through advances in artificial intelligence and machine learning. J Intell Fuzzy Syst (pre-press) 1–8. https://doi.org/10.3233/JIFS-189787

  35. Fatema N et al (2021) Intelligent data-analytics for condition monitoring: smart grid applications. Elsevier, p 268. ISBN: 9780323855112

    Google Scholar 

  36. Smriti S et al (2018) Special issue on intelligent tools and techniques for signals, machines and automation. J Intell Fuzzy Syst 35(5):4895–4899. https://doi.org/10.3233/JIFS-169773

    Article  Google Scholar 

  37. Jafar A et al (2021) AI and machine learning paradigms for health monitoring system: intelligent data analytics. Springer Nature, Berlin, p 49. https://doi.org/10.1007/978-981-33-4412-9. ISBN: 978-981-33-4412-9

  38. Sood YR et al (2019) Applications of artificial intelligence techniques in engineering, vol 1. Springer Nature, p 643. https://doi.org/10.1007/978-981-13-1819-1. ISBN: 978-981-13-1819-1

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Authors and Affiliations

  1. Graphic Era University, Dehradun, India

    Ankit Tomar, Bhaskar Pant, Vikas Tripathi & Saurabh Mishra

  2. College of Engineering Roorkee, Roorkee, India

    Kamal Kant Verma

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  1. Ankit Tomar
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  2. Bhaskar Pant
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  3. Vikas Tripathi
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  4. Kamal Kant Verma
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  5. Saurabh Mishra
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Editor information

Editors and Affiliations

  1. Electrical Engineering Group, Eindhoven University of Technology, Eindhoven, The Netherlands

    Anuradha Tomar

  2. BEARS, University Town, NUS Campus, Singapore, Singapore

    Hasmat Malik

  3. Department of Computer Science and Engineering, Krishna Engineering College, Ghaziabad, India

    Pramod Kumar

  4. Department of Electrical Engineering, Qatar University, Doha, Qatar

    Atif Iqbal

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Tomar, A., Pant, B., Tripathi, V., Verma, K.K., Mishra, S. (2022). Improving QoS of Cloudlet Scheduling via Effective Particle Swarm Model. In: Tomar, A., Malik, H., Kumar, P., Iqbal, A. (eds) Machine Learning, Advances in Computing, Renewable Energy and Communication. Lecture Notes in Electrical Engineering, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-16-2354-7_13

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  • DOI: https://doi.org/10.1007/978-981-16-2354-7_13

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  • Online ISBN: 978-981-16-2354-7

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