Abstract
Nowadays cloud computing is being highly considered by many of researchers, organizations, governments and so on. According to processing happening inside of cloud computing, some of the most important problems and challenges in cloud computing are load balancing, managing resource allocations, scheduling running of tasks. Load balancing on the surface of virtual machines on the internal surface of datacenters, scheduling and resource allocations in hosts and over virtual machines. Thus, by considering existing challenges in cloud computing, in this paper by the help of dragonfly optimization algorithm because of speed and preciseness in scheduling tasks, the process of allocating resources to virtual machines in cloud computing has been done. The proposed method has multiple steps that are as follows: initialization of algorithm and cloud computing, setting number of virtual machines and tasks, running dragonfly optimization algorithm, allocating resources and scheduling tasks with maintaining load balance in virtual machines. By simulation of the proposed method in this research, we observed that the rate of improvement in dragonfly optimization algorithm for resource allocation and keeping load balance between virtual machines is much higher than other methods when considering criterions like execution time, response time, number of migrated tasks and load balance.
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References
K. Ren, C. Wang and Q. Wang, Security Challenges for the Public Cloud, (IEEE Computer Society, 2012), pp.77–96.
A.K. Singh, S. B. Shaw, A Survey on Scheduling and Load Balancing Techniques in Cloud Computing Environment, (International Conference on Computer and Communication Technology (ICCCT), 2014).
P. Mell, T. Grance, The NIST Definition of Cloud Computing, (Draft NIST, 2011).
A. K. Sidhu, S. Kinger, Analysis of Load Balancing Techniques in Cloud Computing, (International Journal of Computers & Technology, Vol. 4, No. 2, 2013), pp. 737–741.
X. Evers, A Literature Study on Scheduling in Distributed Systems, (National Institute voor Kernfysica en Hoge-EnergieFysica P.O. Box 14882, 1009 DB Amsterdam, The Netherlands, 2000).
A. A. Rajguru, S.S. Apte, A Comparative Performance Analysis of Load Balancing Algorithms in Distributed System using Qualitative Parameters, (International Journal of Recent Technology and Engineering, Vol. 1, No. 3, 2012).
N. J. Kansal, I. Chana, Cloud Load Balancing Techniques: A Step towards Green Computing”, (IJCSI international journal of Computer Science, Vol. 9, No. 1, 2012).
S. Sethi, A. Sahu, S. Kumar Jena, Efficient load balancing in Cloud Computing using Fuzzy Logic, (IOSR Journal of Engineering (IOSRJEN), Vol. 2, No. 7, 2012), pp. 65–71.
J. James, B. Verma, Efficient VM Load Balancing Algorithm for a Cloud Computing Environment, (International Journal on Computer Science and Engineering (IJCSE), Vol. 4, No, 3, 2012), pp.1658–1663.
M. Brototi, Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach, (sciverse sciencedirect, C3IT- Procedia Technology, 2012(, pp. 783–789z
Sran, N and Kaur, N, Comparative Analysis of Existing Load Balancing Techniques in Cloud Computing, (International Journal of Engineering Science Invention, Vol. 2, No. 1, 2013).
D. Babu, P. V. Krishna, Honey bee behavior inspired load balancing of tasks in cloud computing environments, (Applied Soft Computing, 2013), pp. 2292–2303.
D. Kliazovich, S. T. Arzo, F. Granelli, P. Bouvry and S. U. Khan, e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing, (IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, 2013), pp. 7–13.
S Mirjalili, Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, (Neural Comput & Applic, 2015).
Misha Goyal, Mehak Aggarwal, Optimize Workflow Scheduling Using Hybrid Ant Colony Optimization (ACO) & Particle Swarm Optimization (PSO) Algorithm in Cloud Environment, (International Journal of Advance research, Ideas and Innovations in Technology, 2017).
V. Polepally, K. S. Chatrapati, Dragonfly optimization and constraint measure-based load balancing in cloud computing, (Cluster Computing, Vol. 1, No. 2, 2017), pp. 1–13.
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Amini, Z., Maeen, M. & Jahangir, M.R. Providing a load balancing method based on dragonfly optimization algorithm for resource allocation in cloud computing. Int J Netw Distrib Comput 6, 35–42 (2018). https://doi.org/10.2991/ijndc.2018.6.1.4
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DOI: https://doi.org/10.2991/ijndc.2018.6.1.4