Analysis of load balancing in cloud data centers

  • Sweekriti M. Shetty
  • Sudheer Shetty
Original Research


Cloud computing is a distributed computing system, where the user will utilize the dynamically provisioned resources including storage, processing, network, etc. This has given rise to cloud data centers, which constitutes virtual resources, that will be shared among multiple users. The major issue in cloud data centers is to handle the millions of simultaneous requests/loads from users. To handle such requests efficiently load balancing algorithms are devised. The incoming load has to be distributed fairly and consistently among the machines which are available. Thus, load balancing techniques deals in achieving high resource utilization by sharing the load efficiently. In this work, Modified Central Load Balancer (MCLB) algorithm is proposed, where the load is balanced among all the available virtual machines thereby avoiding overloading and under loading of virtual machines. Allocation of jobs is done by considering the priority and the state of the virtual machine which helps in the fair allocation of the jobs and efficient user utilization. The MCLB algorithm is simulated using CloudSim and it is compared with existing Round Robin algorithm, Throttled algorithm and Equally Spread Current Execution Load algorithm. The comparison analysis shows that MCLB outperforms the remaining in performance evaluation metrics such as response time, data center processing time and total cost.



  1. Abdullah M, Othman M (2013) Cost-based multi-qos job scheduling using divisible load theory in cloud computing. Procedia Comput Sci 18:928–935CrossRefGoogle Scholar
  2. Adhikari J, Patil S (2013) Double threshold energy aware load balancing in cloud computing. In 2013 fourth international conference on computing, communications and networking technologies (ICCCNT) (pp 1–6)Google Scholar
  3. Al Nuaimi K, Mohamed N, Al Nuaimi M, Al-Jaroodi J (2012) A survey of load balancing in cloud computing: challenges and algorithms. In: Network cloud computing and applications (NCCA), 2012 second symposium on (pp 137–142)Google Scholar
  4. Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softwa Pract Exp 41(1):23–50CrossRefGoogle Scholar
  5. Cao J, Li K, Stojmenovic I (2014) Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. IEEE Trans Comput 63(1):45–58MathSciNetCrossRefGoogle Scholar
  6. Carlini E, Ricci L, Coppola M (2013) Flexible load distribution for hybrid distributed virtual environments. Future Gener Comput Syst 29(6):1561–1572CrossRefGoogle Scholar
  7. Domanal SG, Reddy GRM (2013) Load balancing in cloud computing using modified throttled algorithm. In: Cloud computing in emerging markets (CCEM), 2013 IEEE international conference on (pp 1–5)Google Scholar
  8. Fan Y, Wu W, Xu Y, Chen H (2014) Improving mapreduce performance by balancing skewed loads. Commun China 11(8):85–108CrossRefGoogle Scholar
  9. Fang Y, Wang F, Ge J (2010) A task scheduling algorithm based on load balancing in cloud computing. In: Web information systems and mining. Springer, pp 271–277Google Scholar
  10. Fernandes SL, Gurupur VP, Sunder NR, Arunkumar N, Kadry S (2017) A novel nonintrusive decision support approach for heart rate measurement. Pattern Recogn LettGoogle Scholar
  11. Ghafarian T, Javadi B (2014) Cloud-aware data intensive work flow scheduling on volunteer computing systems. Future Gener Comput Syst 51:87CrossRefGoogle Scholar
  12. Hsiao H-C, Chung H-Y, Shen H, Chao Y-C (2013) Load rebalancing for distributed file systems in clouds. Parallel Distrib Syst IEEE Trans 24(5):951–962CrossRefGoogle Scholar
  13. Jain A, Yadav A, Namboodiri L, Abraham J (2013) A threshold band based model for automatic load balancing in cloud environment. In: Cloud computing in emerging markets (CCEM), 2013 IEEE international conference on, pp 1–7Google Scholar
  14. Khara S, Thakkar U (2017) A novel approach for enhancing selection of load balancing algorithms dynamically in cloud computing. In: Computer, communications and electronics (comptelix), 2017 international conference on, pp 44–48Google Scholar
  15. Liang P-H, Yang J-M (2013) Evaluation of cloud hybrid load balancer (CHLB). Int J E-Bus Dev 41:23Google Scholar
  16. Luo J, Rao L, Liu X (2014) Temporal load balancing with service delay guarantees for data center energy cost optimization. Parallel Distrib Syst IEEE Trans 25(3):775–784CrossRefGoogle Scholar
  17. Maguluri ST, Srikant R (2014) Scheduling jobs with unknown duration in clouds. IEEE/ACM Trans Netw 22(6):1938–1951CrossRefGoogle Scholar
  18. Maguluri ST, Srikant R, Ying L (2014) Heavy traffic optimal resource allocation algorithms for cloud computing clusters. Perform Eval 81:20–39CrossRefGoogle Scholar
  19. Mahajan K, Makroo A, Dahiya D (2013) Round robin with server affinity: a VM load balancing algorithm for cloud based infrastructure. J Inf Process Syst 9(3):379–394CrossRefGoogle Scholar
  20. Mashaly M, Kuhn PJ (2012) Load balancing in cloud-based content delivery networks using adaptive server activation/deactivation. In: Engineering and technology (ICET), 2012 international conference on, pp 1–6Google Scholar
  21. Nakrani S, Tovey C (2004) On honey bees and dynamic server allocation in internet hosting centers. Adapt Behav 12(3–4):223–240CrossRefGoogle Scholar
  22. Nishant K, Sharma P, Krishna V, Gupta C, Singh KP, Rastogi R et al (2012) Load balancing of nodes in cloud using ant colony optimization. In: 2012 14th international conference on modelling and simulation, pp 3–8Google Scholar
  23. Randles M, Lamb D, Taleb-Bendiab A (2010) A comparative study into distributed load balancing algorithms for cloud computing. In: Advanced information networking and applications workshops (WAINA), 2010 IEEE 24th international conference on, pp 551–556Google Scholar
  24. Rodriguez MA, Buyya R (2014) Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans Cloud Comput 2(2):222–235CrossRefGoogle Scholar
  25. Sanaei Z, Abolfazli S, Gani A, Buyya R (2014) Heterogeneity in mobile cloud computing: taxonomy and open challenges. IEEE Commun Surv Tutor 16(1):369–392CrossRefGoogle Scholar
  26. Tai J, Li Z, Chen J, Mi N (2014) Load balancing for cluster systems under heavy-tailed and temporal dependent workloads. Simul Modell Pract Theory 44:63–77CrossRefGoogle Scholar
  27. Tokle S, Bellipady SR, Ranjan R, Varma S (2014) Energy-efficient wireless sensor networks using learning techniques. Achievements and trends, case studies in intelligent computing, pp 407–426Google Scholar
  28. Wang Z, Chen H, Fu Y, Liu D, Ban Y (2014) Workload balancing and adaptive resource management for the swift storage system on cloud. Future Gener Comput Syst 51:120CrossRefGoogle Scholar
  29. Xu G, Pang J, Fu X (2013) A load balancing model based on cloud partitioning for the public cloud. Tsinghua Sci Technol 18(1):34–39CrossRefGoogle Scholar
  30. Zhang H, Jiang G, Yoshihira K, Chen H (2014) Proactive workload management in hybrid cloud computing. Netw Serv Manag IEEE Trans 11(1):90–100CrossRefGoogle Scholar
  31. Zhao J, Yang K, Wei X, Ding Y, Hu L, Xu G (2016) A heuristic clustering-based task deployment approach for load balancing using bayes theorem in cloud environment. IEEE Trans Parallel Distrib Syst 27(2):305–316CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Sahyadri College of Engineering and ManagementAdyarIndia

Personalised recommendations