Comparative study on load balancing and service broker algorithms in Cloud computing using cloud analyst tool


Payaswini P


In recent years, there has been a large increase in the number of cloud users as it provides an easy and flexible way to manage user data and applications. With the emerging technologies such as the Internet of things, cloud computing being the backbone, the load on the cloud servers has increased. The cloud data centers consist of servers hosting multiple virtual machines. One of the main challenges in cloud computing is to efficiently distribute the user service requests to different virtual machines in order to reduce the request processing time and to provide more user satisfaction. Load balancing algorithms basically address two issues: the selection of the data center and the distribution of the load on different virtual machines. Data center selection is handled by service broker policy and distribution of the load on virtual machines handled by VM load balancer. Load balancing techniques play a vital role in minimizing the response time and maximizing throughput and also ensure scalability and reliability. Hence, it has become an important research topic in the field of cloud computing. Cloud-analyst, a java based open source toolkit, is useful to simulate and analyze the load balancing algorithms. In this paper, a comparative study on different service broker policies and VM load balancing algorithms for cloud computing is presented with simulation results. The aim of this comparative study is to find the performance of different service broker policies and the load balancing algorithm tested on different scenarios.


How to Cite
Payaswini P. (2021). Comparative study on load balancing and service broker algorithms in Cloud computing using cloud analyst tool. International Journal of Next-Generation Computing, 12(1), 49–61.


  1. Mell, P., & Grance, T. (2009). Draft nist working definition of cloud computing-v15. 21. Aug 2009, 2, 123-135.
  2. Hogan, M., Liu, F., Sokol, A., & Tong, J. (2011). Nist cloud computing standards roadmap. NIST Special Publication, 35, 6-11.
  3. Chun, S. H., & Choi, B. S. (2014). Service models and pricing schemes for cloud computing. Cluster Computing, 17(2), 529-535.
  4. Bojanova, I., & Samba, A. (2011, March). Analysis of cloud computing delivery architecture models. In 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications (pp. 453-458). IEEE.
  5. Jadeja, Y., & Modi, K. (2012, March). Cloud computing-concepts, architecture and challenges. In 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET) (pp. 877-880). IEEE.
  6. Xing, Y., & Zhan, Y. (2012). Virtualization and cloud computing. In Future Wireless Networks and Information Systems (pp. 305-312). Springer, Berlin, Heidelberg.
  7. Xu, M., Tian, W., & Buyya, R. (2017). A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurrency and Computation: Practice and Experience, 29(12), e4123.
  8. Tiwari, P. K., & Joshi, S. (2016). A review on load balancing of virtual machine resources in cloud computing. In Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2 (pp. 369-378). Springer, Cham.
  9. Al Nuaimi, K., Mohamed, N., Al Nuaimi, M., & Al-Jaroodi, J. (2012, December). A survey of load balancing in cloud computing: Challenges and algorithms. In 2012 second symposium on network cloud computing and applications (pp. 137-142). IEEE.
  10. Jyoti, A., Shrimali, M., Tiwari, S., & Singh, H. P. (2020). Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey. Journal of Ambient Intelligence and Humanized Computing, 1-30.
  11. Manasrah, A. M., Smadi, T., & ALmomani, A. (2017). A variable service broker routing policy for data center selection in cloud analyst. Journal of King Saud University-Computer and Information Sciences, 29(3), 365-377.
  12. Kumar, A., & Kalra, M. (2016, April). Load balancing in cloud data center using modified active monitoring load balancer. In 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA)(Spring) (pp. 1-5). IEEE.
  13. Yongjun, L. LiXiaole, and Sun Ruxiang, (2008), “Load Balancing Algorithms Overview,”. Information Development and Economy, 18, 134-136.
  14. Randles, M., Lamb, D., & Taleb-Bendiab, A. (2010, April). A comparative study into distributed load balancing algorithms for cloud computing. In 2010 IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (pp. 551-556). IEEE.
  15. Wickremasinghe, B., Calheiros, R. N., & Buyya, R. (2010, April). Cloudanalyst: A cloudsim-based visual modeller for analysing cloud computing environments and applications. In 2010 24th IEEE international conference on advanced information networking and applications (pp. 446-452). IEEE.
  16. Dillon, T., Wu, C., & Chang, E. (2010, April). Cloud computing: issues and challenges. In 2010 24th IEEE international conference on advanced information networking and applications (pp. 27-33). Ieee.
  17. Aguiar, E., Zhang, Y., & Blanton, M. (2014). An overview of issues and recent developments in cloud computing and storage security. In High Performance Cloud Auditing and Applications (pp. 3-33). Springer, New York, NY.
  18. Yang, J., & Chen, Z. (2010, December). Cloud computing research and security issues. In 2010 International Conference on Computational Intelligence and Software Engineering (pp. 1-3). IEEE.
  19. Mishra, S. K., Sahoo, B., & Parida, P. P. (2018). Load balancing in cloud computing: a big picture. Journal of King Saud University-Computer and Information Sciences.
  20. Katyal, M., & Mishra, A. (2014). A comparative study of load balancing algorithms in cloud computing environment. arXiv preprint arXiv:1403.6918.
  21. Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A., & Buyya, R. (2011). CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience, 41(1), 23-50.
  22. Buyya, R. (2009). CloudAnalyst: A CloudSim-based tool for modelling and analysis of large scale cloud computing environments. Distrib. Comput. Proj. Csse Dept., Univ. Melb., 433-659.
  23. Mishra, R. K., & Bhukya, S. N. (2014). Service broker algorithm for cloud-analyst. International Journal of Computer Science and Information Technologies, 5(3), 3957-3962.
  24. Limbani, D., & Oza, B. (2012). A proposed service broker policy for data center selection in cloud environment with implementation. International Journal of Computer Technology & Applications, 3(3), 1082-1087.
  25. Benlalia, Z., Beni-hssane, A., Abouelmehdi, K., & Ezati, A. (2019). A new service broker algorithm optimizing the cost and response time for cloud computing. Procedia Computer Science, 151, 992-997.
  26. Rekha, P. M., & Dakshayini, M. (2014, April). Cost based data center selection policy for large scale networks. In 2014 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC) (pp. 18-23). IEEE.
  27. Mishra, R. K., Kumar, S., & Naik, B. S. (2014, February). Priority based round-robin service broker algorithm for cloud-analyst. In 2014 IEEE International Advance Computing Conference (IACC) (pp. 878-881). IEEE.
  28. Rani, P., Chauhan, R., & Chauhan, R. (2015). An enhancement in service broker policy for cloud-analyst. International Journal of Computer Applications, 115(12).
  29. Phi, N. X., Tin, C. T., Thu, L. N. K., & Hung, T. C. (2018). Proposed load balancing algorithm to reduce response time and processing time on cloud computing. Int. J. Comput. Netw. Commun, 10(3), 87-98.
  30. Mohapatra, S., Rekha, K. S., & Mohanty, S. (2013). A comparison of four popular heuristics for load balancing of virtual machines in cloud computing. International Journal of Computer Applications, 68(6).
  31. Patel, S., Patel, R., Patel, H., & Vahora, S. (2015). CloudAnalyst: a survey of load balancing policies. International Journal of Computer Applications, 117(21).
  32. Domanal, S. G., & Reddy, G. R. M. (2013, October). Load balancing in cloud computingusing modified throttled algorithm. In 2013 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) (pp. 1-5). IEEE.
  33. Dam, S., Mandal, G., Dasgupta, K., & Dutta, P. (2014). An ant colony based load balancing strategy in cloud computing. In Advanced Computing, Networking and Informatics-Volume 2 (pp. 403-413). Springer, Cham.
  34. Pandey, S., Wu, L., Guru, S. M., & Buyya, R. (2010, April). 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 (pp. 400-407). IEEE.
  35. Ramezani, F., Lu, J., & Hussain, F. K. (2014). Task-based system load balancing in cloud computing using particle swarm optimization. International journal of parallel programming, 42(5), 739-754.
  36. Zhu, Y., Zhao, D., Wang, W., & He, H. (2016, January). A novel load balancing algorithm based on improved particle swarm optimization in cloud computing environment. In International Conference on Human Centered Computing (pp. 634-645). Springer, Cham.
  37. LD, D. B., & Krishna, P. V. (2013). Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied Soft Computing, 13(5), 2292-2303.
  38. Patel, H., & Patel, R. (2015). Cloud analyst: an insight of service broker policy. International Journal of Advanced Research in Computer and Communication Engineering, 4(1), 122-127.
  39. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
  40. Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). IEEE.
  41. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE computational intelligence magazine, 1(4), 28-39.