Adaptive task scheduling method in multi-tenant cloud computing


Cloud security is the primary need for the vital Information Technology industry. It adopts dynamic qualities and enhances various heterogeneous resources for its applications. Cloud environment enables virtual technologies using virtual machine placement method. Therefore any virtual machines can move between any physical devices for achieving cost optimization and network traffic minimization sake. Multi-tenancy means the use of multiple systems applications or data from various organizations residing on a single physical device. Here a single instance of the application software running on the service providers’ platform can be accessed by multiple clients simultaneously. Multi-tenancy concept refers to both public as well as private cloud model which relates to all the three layers in cloud computing system. Adaptive particle swarm optimization is proposed in this paper which also addresses the multi-tenancy process which enables high resource utilization service under cloud storage network.

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  1. 1.

    Shahapure NH, Jayarekha P (2014, April) Load balancing with optimal cost scheduling algorithm. In: 2014 International conference on computation of power, energy, information and communication (ICCPEIC). IEEE, pp 24–31

  2. 2.

    Kim SH, Kang DK, Kim WJ, Chen M, Youn CH (2017) A science gateway cloud with cost-adaptive VM management for computational science and applications. IEEE Syst J 11(1):173–185

    Article  Google Scholar 

  3. 3.

    Garg SK, Toosi AN, Gopalaiyengar SK, Buyya R (2014) SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J Netw Comput Appl 45:108–120

    Article  Google Scholar 

  4. 4.

    Tesfatsion SK, Wadbro E, Tordsson J (2014) A combined frequency scaling and application elasticity approach for energy-efficient cloud computing. Sustain Comput Inf Syst 4(4):205–214

    Google Scholar 

  5. 5.

    Wei L, Zhu H, Cao Z, Dong X, Jia W, Chen Y, Vasilakos AV (2014) Security and privacy for storage and computation in cloud computing. Inf Sci 258:371–386

    Article  Google Scholar 

  6. 6.

    Sood SK (2012) A combined approach to ensure data security in cloud computing. J Netw Comput Appl 35(6):1831–1838

    Article  Google Scholar 

  7. 7.

    Ashalatha R, Agarkhed J (2015, December) Dynamic load balancing methods for resource optimization in cloud computing environment. In: 2015 Annual IEEE India conference (INDICON). IEEE, pp 1–6

  8. 8.

    Xiao Z, Song W, Chen Q (2013) Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parallel Distrib Syst 24(6):1107–1117

    Article  Google Scholar 

  9. 9.

    Hashizume K, Rosado DG, Fernández-Medina E, Fernandez EB (2013) An analysis of security issues for cloud computing. J Internet Serv Appl 4(1):5

    Article  Google Scholar 

  10. 10.

    Frincu ME, Stéphane G, Julien G (2013) Comparing provisioning and scheduling strategies for workflows on clouds. In: 2013 IEEE 27th international parallel and distributed processing symposium workshops & Ph.D. Forum (IPDPSW). IEEE

  11. 11.

    Rahman M et al (2013) Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurr Comput Pract Exp 25(13):1816–1842

    Article  Google Scholar 

  12. 12.

    Gonzalez N, Miers C, Redigolo F, Simplicio M, Carvalho T, Näslund M, Pourzandi M (2012) A quantitative analysis of current security concerns and solutions for cloud computing. J Cloud Comput Adv Syst Appl 1(1):11

    Article  Google Scholar 

  13. 13.

    Xiao Z, Xiao Y (2013) Security and privacy in cloud computing. IEEE Commun Surv Tutor 15(2):843–859

    Article  Google Scholar 

  14. 14.

    Buyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308

  15. 15.

    Lu K, Yahyapour R, Wieder P, Kotsokalis C, Yaqub E, Jehangiri AI (2013, June) Qos-aware vm placement in multi-domain service level agreements scenarios. In: 2013 IEEE sixth international conference on cloud computing (CLOUD). IEEE, pp 661–668

  16. 16.

    AlJahdali H, Albatli A, Garraghan P, Townend P, Lau L, Xu J (2014, April) Multi-tenancy in cloud computing. In: 2014 IEEE 8th international symposium on service oriented system engineering (SOSE). IEEE, pp 344–351

  17. 17.

    Ashalatha R, Agarkhed J (2016, March) Multi tenancy issues in cloud computing for SaaS environment. In 2016 International conference on circuit, power and computing technologies (ICCPCT). IEEE, pp 1–4

  18. 18.

    Meng X, Pappas V, Zhang L (2010, March) Improving the scalability of data center networks with traffic-aware virtual machine placement. In: 2010 Proceedings IEEE INFOCOM. IEEE, pp 1–9

  19. 19.

    Duan J, Yang Y (2017) A load balancing and multi-tenancy oriented data center virtualization framework. IEEE Trans Parallel Distrib Syst 28(8):2131–2144

    Article  Google Scholar 

  20. 20.

    Rimal BP, Maier M (2017) Workflow scheduling in multi-tenant cloud computing environments. IEEE Trans Parallel Distrib Syst 28(1):290–304

    Article  Google Scholar 

  21. 21.

    Li X, Qian C (2015, June) Traffic and failure aware VM placement for multi-tenant cloud computing. In: 2015 IEEE 23rd International symposium on quality of service (IWQoS). IEEE, pp 41–50

  22. 22.

    Ferdaus MH, Murshed M, Calheiros RN, Buyya R (2017) An algorithm for network and data-aware placement of multi-tier applications in cloud data centers. J Netw Comput Appl 98:65–83

    Article  Google Scholar 

  23. 23.

    Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. The Brazilian Computer Society, Springer, Berlin, pp 7–18

    Google Scholar 

  24. 24.

    EJ Domingo et al. (2010) CLOUDIO: a cloud computing-oriented multi-tenant architecture for business information systems. In: Proceedings of IEEE 3rd international conference on cloud computing (CLOUD), pp 532–533

  25. 25.

    Dillon T, Wu C, Chang E (2010) Cloud computing: issues and challenges. In: Proceedings of 2010 24th IEEE international conference on advanced information networking and applications. IEEE, pp 27–33

  26. 26.

    Ashalatha R, Agarkhed J (2015) Evaluation of auto scaling and load balancing features in cloud. Int J Comput Appl 117(6):30–33

    Google Scholar 

  27. 27.

    Pathirage M, Perera S, Kumara I, Weerawarana S (2011, July) A multi-tenant architecture for business process executions. In: 2011 IEEE international conference on Web services (ICWS). IEEE, pp 121–128

  28. 28.

    Bharti K, Kamaljit K (2014) A survey of resource allocation techniques in cloud computing. IJACECT 3:31–35

    Google Scholar 

  29. 29.

    Afoulki Z, Bousquet A, Rouzaud-Cornabas J (2011) A security-aware scheduler for virtual machines on IAAS clouds. Report 2011

  30. 30.

    Buyya, R, Saurabh KG, Calheiros RN (2011) SLA-oriented resource provisioning for cloud computing: challenges, architecture, and solutions. In: 2011 International conference on cloud and service computing (CSC). IEEE

  31. 31.

    Zou M, He J, Wu Q (2016, December) Multi-tenancy access control strategy for cloud services. In: 2016 10th International conference on software, knowledge, information management & applications (SKIMA). IEEE, pp 258–261

  32. 32.

    Liu Z, Wang X (2012, June) A PSO-based algorithm for load balancing in virtual machines of cloud computing environment. In International conference in swarm intelligence. Springer, Berlin, pp. 142-147

  33. 33.

    Kannan S, Rani TS (2017) An empirical study of efficient resource allocation using modified particle swarm optimization in cloud environment. Jour of Adv Research in Dynamical & Control Systems, 15-Special Issu pp 951–955

  34. 34.

    Khan AA, Khan M, Ahmed W (2016, September) Improved scheduling of virtual machines on cloud with multi-tenancy and resource heterogeneity. In: International conference on automatic control and dynamic optimization techniques (ICACDOT). IEEE. Task Scheduling in Cloud Computing Environment: A Comprehensive Analysis, pp 815–819

  35. 35.

    Belgacem A, Beghdad-Bey K, Nacer H (2018, April) Task scheduling in cloud computing environment: a comprehensive analysis. In: International conference on computer science and its applications. Springer, Cham, pp 14–26

  36. 36.

    Luo J, Song W, Yin L (2018) Reliable virtual machine placement based on multi-objective optimization with traffic-aware algorithm in industrial cloud. IEEE Access 6:23043–23052

    Article  Google Scholar 

  37. 37.

    Liu L, Fan Q, Buyya R (2018) A deadline-constrained multi-objective task scheduling algorithm in mobile cloud environments. IEEE Access 6:52982–52996

    Article  Google Scholar 

  38. 38.

    Mishra N, Siddiqui S, Tripathi JP (2015) A compendium over cloud computing cryptographic algorithms and security issues. BVICA M’s Int J Inf Technol 7(1):810

    Google Scholar 

  39. 39.

    Sonkar SK, Kharat MU (2019) Load prediction analysis based on virtual machine execution time using optimal sequencing algorithm in cloud federated environment. Int J Inf Technol 11(2):265–275

    Google Scholar 

  40. 40.

    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 

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Correspondence to Ashalatha Ramegowda.

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Ramegowda, A., Agarkhed, J. & Patil, S.R. Adaptive task scheduling method in multi-tenant cloud computing. Int. j. inf. tecnol. 12, 1093–1102 (2020).

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  • Cloud computing
  • Virtual machines
  • Quality of service
  • Multi-tenancy
  • VM scheduling