Abstract
Cloud computing is a developing technology that enables on-demand network access to the users through a shared pool of cluster computing resources. However, maintaining the stability of processing several tasks in the cloud environment is a complex issue. Hence, it requires a load balancing technique that allocates the task to the Virtual Machines (VMs) without affecting the performance of the system. This paper presents a technique for load balancing, called fractional dragonfly based load balancing algorithm (FDLA), by proposing two selection probabilities and fractional dragonfly algorithm. The proposed load balancing model utilizes certain parameters of VMs and Physical Machines (PMs) to select the tasks to be reallocated in the VMs for load balancing. The selection is based on the probabilities, Task selection probability (TSP) and VM selection probability (VSP), which are newly designed. Further, the proposed fractional dragonfly algorithm that combines dragonfly algorithm (DA) with Fractional Calculus (FC) performs an optimal selection of VMs for the reallocation of the task using a newly designed fitness function. In the performance analysis of FDLA based on load and number of tasks reallocated, the proposed FDLA could achieve a minimum load of 0.2133 with 14 reallocated tasks, indicating its effectiveness in load balancing.
Similar content being viewed by others
References
Chen, S.L., Chen, Y.Y., Kuo, S.H.: CLB: a novel load balancing architecture and algorithm for cloud services. Comput. Electr. Eng. 58, 154–160 (2017)
Naha, R.K., Othman, M.: Cost aware service brokering and performance sentient load balancing algorithms in the cloud. J. Netw. Comput. Appl. 75, 47–57 (2016)
Bernstein, D., Vij, D., Diamond, S.: An inter-cloud cloud computing economy-technology, governance, and market blueprints. In: SRII Global Conference (SRII), pp. 293–299, San Jose, CA, USA (2011)
Bernstein, D., Ludvigson, E., Sankar, K., Diamond, S., Morrow, M.: Blue print for the intercloud-protocols and formats for cloud computing interoperability. In: Proceedings of a Fourth International Conference on Internet and Web Applications and Services, Venice, Italy, pp. 328–336 (2009)
Ghumman, N.S., Kaur, R.: Dynamic combination of improved max-min and ant colony algorithm for load balancing in the cloud system. In: Proceedings of 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Denton, TX, USA, pp. 1–5 (2015)
Desai, T., Prajapati, J.: A survey of various load balancing techniques and challenges in cloud computing. Int. J. Sci. Technol. Res. 2(11), 158–161 (2013)
Dhinesh Babu, L.D., Krishna, P.V.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303 (2013)
Eager, D.L., Lazowska, E.D., Zahorjan, J.: Adaptive load sharing in homogeneous distributed systems. IEEE Trans. Softw. Eng. 12(5), 662–675 (1986)
Razzaghzadeh, S., Navin, A.H., Rahmani, A.M., Hosseinzadeh, M.: Probabilistic modeling to achieve load balancing in expert clouds. Ad Hoc Netw. 59, 12–23 (2017)
Sidhu, A., Kinger, S.: Analysis of load balancing techniques in cloud computing. Int. J. Comput. Technol. 4(2), 2277–3061 (2013)
Zhao, J., Yang, K., Wei, X., Ding, Y., Liang, H., Gaochao, X.: A heuristic clustering-based task deployment approach for load balancing using bayes theorem in cloud environment. IEEE Trans. Parallel Distrib. Syst. 27(2), 305–316 (2016)
Willebeek-LeMair, M.H., Reeves, A.P.: Strategies for dynamic load balancing on highly parallel computers. IEEE Trans. Parallel Distrib. Syst. 4(9), 979–993 (1993)
You, T., Li, W., Fang, Z., Wang, H., Qu, G.: Performance evaluation of dynamic load balancing algorithms. Indones. J. Electr. Eng. 12(4), 2850–2859 (2014)
Bahi, J.M., Contassot-Vivier, S., Couturier, R.: Dynamic load balancing and efficient load estimators for asynchronous iterative algorithms. IEEE Trans. Parallel Distrib. Syst. 16(4), 289–299 (2005)
Chander, S., Vijaya, P., Dhyani, P.: Fractional lion algorithm—an optimization algorithm for data clustering. J. Comput. Sci. 12(7), 323–340 (2016)
Chander, P.D.S., Vijaya, P.: DOFL: Kernel based directive operative fractional line optimization algorithm for data clustering. Int. Rev. Comput. Softw. (IRECOS). 11(8), 701–714 (2016)
Liu, Q., Cai, W., Shen, J., Liu, X., Linge, N.: An adaptive approach to better load balancing in a consumer-centric cloud environment. IEEE Trans. Consum. Electron. 62(3), 243–250 (2016)
Xu, G., Pang, J., Fu, X.: A load balancing model based on cloud partitioning for the public cloud. Tsinghua Sci. Technol. 18(1), 34–39 (2013)
Rastegarfar, H., Rusch, L.A., Leon-Garcia, A.: Optical load-balancing tradeoffs in wavelength-routing cloud data centers. IEEE/OSA J. Opt. Commun. Netw 7(4), 286–300 (2015)
Kaur, R., Luthra, P.: Load balancing in cloud computing. In: Proceedings of International Conference on Recent Trends in Information, Telecommunication and Computing, ITC (2012)
Xu, G., Ding, Y., Zhao, J., Hu, L., Fu, X.: A novel artificial bee colony approach of live virtual machine migration policy using Bayes theorem. Sci. World J. 2013, 1–13 (2013)
Zhao, J., Hu, L., Ding, Y., Xu, G., Hu, M.: A heuristic placement selection of live virtual machine migration for energy-saving in cloud computing environment. PloS ONE 9(9), e108275 (2014)
Mirjali, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2016)
Bhaladhare, P.R., Jinwala, D.C.: A clustering approach for the -diversity model in privacy preserving data mining using fractional calculus l-bacterial foraging optimization algorithm. Adv. Comput. Eng. 2014, 1–12 (2014)
Ramezani, F., Lu, J., Hussain, F.K.: Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parallel Prog. 42(5), 739–754 (2014)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ashok Kumar, C., Vimala, R., Aravind Britto, K.R. et al. FDLA: Fractional Dragonfly based Load balancing Algorithm in cluster cloud model. Cluster Comput 22 (Suppl 1), 1401–1414 (2019). https://doi.org/10.1007/s10586-018-1977-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-1977-6