Adaptive task scheduling method in multi-tenant cloud computing

  • Ashalatha RamegowdaEmail author
  • Jayashree Agarkhed
  • Siddarama R. Patil
Original Research


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.


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


  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 24–31Google Scholar
  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):173–185CrossRefGoogle 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 45:108–120CrossRefGoogle 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):205–214Google 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 258:371–386CrossRefGoogle Scholar
  6. 6.
    Sood SK (2012) A combined approach to ensure data security in cloud computing. J Netw Comput Appl 35(6):1831–1838CrossRefGoogle 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 1–6Google Scholar
  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):1107–1117CrossRefGoogle 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):5CrossRefGoogle 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). IEEEGoogle Scholar
  11. 11.
    Rahman M et al (2013) Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurr Comput Pract Exp 25(13):1816–1842CrossRefGoogle 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):11CrossRefGoogle Scholar
  13. 13.
    Xiao Z, Xiao Y (2013) Security and privacy in cloud computing. IEEE Commun Surv Tutor 15(2):843–859CrossRefGoogle 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 661–668Google Scholar
  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 344–351Google Scholar
  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 1–4Google Scholar
  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 1–9Google Scholar
  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):2131–2144CrossRefGoogle Scholar
  20. 20.
    Rimal BP, Maier M (2017) Workflow scheduling in multi-tenant cloud computing environments. IEEE Trans Parallel Distrib Syst 28(1):290–304CrossRefGoogle 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 41–50Google Scholar
  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 98:65–83CrossRefGoogle 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 7–18Google 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 532–533Google Scholar
  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 27–33Google Scholar
  26. 26.
    Ashalatha R, Agarkhed J (2015) Evaluation of auto scaling and load balancing features in cloud. Int J Comput Appl 117(6):30–33Google 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 121–128Google Scholar
  28. 28.
    Bharti K, Kamaljit K (2014) A survey of resource allocation techniques in cloud computing. IJACECT 3:31–35Google Scholar
  29. 29.
    Afoulki Z, Bousquet A, Rouzaud-Cornabas J (2011) A security-aware scheduler for virtual machines on IAAS clouds. Report 2011Google Scholar
  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). IEEEGoogle Scholar
  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 258–261Google Scholar
  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. 142-147Google Scholar
  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 951–955Google Scholar
  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 815–819Google Scholar
  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 14–26Google Scholar
  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 6:23043–23052CrossRefGoogle Scholar
  37. 37.
    Liu L, Fan Q, Buyya R (2018) A deadline-constrained multi-objective task scheduling algorithm in mobile cloud environments. IEEE Access 6:52982–52996CrossRefGoogle 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):810Google 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):265–275Google 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):119–125Google Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.Department of C.S.E.P.D.A. College of EngineeringKalaburagiIndia
  2. 2.Department of E&CEP.D.A. College of EngineeringKalaburagiIndia

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