Access the full text.
Sign up today, get DeepDyve free for 14 days.
Q. Qi, Jingyu Wang, Qi Li, Tonghong Li, Yufei Cao (2016)
Resource orchestration for multi-task application in home-to-home cloudIEEE Transactions on Consumer Electronics, 62
Xiong Fu, Juzhou Chen, Song Deng, Junchang Wang, Lin Zhang (2018)
Layered virtual machine migration algorithm for network resource balancing in cloud computingFrontiers of Computer Science, 12
Luwei Cheng, F. Lau (2016)
Offloading Interrupt Load Balancing from SMP Virtual Machines to the HypervisorIEEE Transactions on Parallel and Distributed Systems, 27
Tingting Wang, Zhaobin Liu, Yi Chen, Yujie Xu, Xiaoming Dai (2014)
Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing
Cheng Qin, Wei Ni, Hui Tian, R. Liu (2017)
Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access NetworksIEEE Access, 5
Sivaraman Eswaran, Manickachezian Rajakannu (2017)
Multiservice Load Balancing with Hybrid Particle Swarm Optimization in Cloud-Based Multimedia Storage System with QoS ProvisionMobile Networks and Applications, 22
Shanhe Jiang, Zhicheng Ji, Yanxia Shen (2014)
A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraintsInternational Journal of Electrical Power & Energy Systems, 55
Xiaolong Xu, Lingling Cao, Xinheng Wang (2016)
Resource pre-allocation algorithms for low-energy task scheduling of cloud computingJournal of Systems Engineering and Electronics, 27
R. Naha, M. Othman (2016)
Cost-aware service brokering and performance sentient load balancing algorithms in the cloudJ. Netw. Comput. Appl., 75
(2016)
16 – Big data analytics and cloud computing for sustainable building energy efficiency
S. Mirjalili (2016)
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsNeural Computing and Applications, 27
Shridhar Damanal, G. Ram, M. Reddy (2014)
Optimal load balancing in cloud computing by efficient utilization of virtual machines2014 Sixth International Conference on Communication Systems and Networks (COMSNETS)
N. Moganarangan, R. Babukarthik, S. Bhuvaneswari, M. Basha, D. Ponnurangam (2016)
A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approachJ. King Saud Univ. Comput. Inf. Sci., 28
L. Babu, P. Krishna (2013)
Honey bee behavior inspired load balancing of tasks in cloud computing environmentsAppl. Soft Comput., 13
Scintami Dam, G. Mandal, K. Dasgupta, P. Dutta (2015)
Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computingProceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)
Ashkan Paya, D. Marinescu (2017)
Energy-Aware Load Balancing and Application Scaling for the Cloud EcosystemIEEE Transactions on Cloud Computing, 5
Alireza Milani, N. Navimipour (2016)
Load balancing mechanisms and techniques in the cloud environments: Systematic literature review and future trendsJ. Netw. Comput. Appl., 71
Junwei Ge, Chao Liao, Yiqiu Fang (2012)
Research of semantic search algorithm based on Cloud computing
Ningning Song, Gong Chao, Xingshuo An, Zhan Qiang (2016)
Fog computing dynamic load balancing mechanism based on graph repartitioningChina Communications, 13
Yong Liu, Liang Ma (2013)
Improved gravitational search algorithm based on free search differential evolutionJournal of Systems Engineering and Electronics, 24
Daniel Merkle, M. Middendorf, H. Schmeck (2000)
Ant colony optimization for resource-constrained project schedulingIEEE Trans. Evol. Comput., 6
Hong Zhong, Yaming Fang, Jie Cui (2017)
LBBSRT: An efficient SDN load balancing scheme based on server response timeFuture Gener. Comput. Syst., 68
S. Chander, P. Vijaya, P. Dhyani (2016)
DOFL: Kernel Based Directive Operative Fractional Lion Optimisation Algorithm for Data ClusteringInternational Review on Computers and Software, 11
Filipe Matos, J. Celestino, A. Cardoso (2015)
VBalance: A selection policy of virtual machines for load balancing in cloud computing2015 IEEE Symposium on Computers and Communication (ISCC)
Yongqiang Gao, Haibing Guan, Zhengwei Qi, Yang Hou, Liang Liu (2013)
A multi-objective ant colony system algorithm for virtual machine placement in cloud computingJ. Comput. Syst. Sci., 79
(2014)
Adaptive firefly optimization on reducing high dimensional weighted word affinity graph
This paper aims to develop the Dragonfly-based exponential gravitational search algorithm to VMM strategy for effective load balancing in cloud computing. Due to widespread growth of cloud users, load balancing is the essential criterion to deal with the overload and underload problems of the physical servers. DEGSA-VMM is introduced, which calculates the optimized position to perform the virtual machine migration (VMM).Design/methodology/approachThis paper presents an algorithm Dragonfly-based exponential gravitational search algorithm (DEGSA) that is based on the VMM strategy to migrate the virtual machines of the overloaded physical machine to the other physical machine keeping in mind the energy, migration cost, load and quality of service (QoS) constraints. For effective migration, a fitness function is provided, which selects the best fit that possess minimum energy, cost, load and maximum QoS contributing toward the maximum energy utilization.FindingsFor the performance analysis, the experimentation is performed with three setups, with Setup 1 composed of three physical machines with 12 virtual machines, Setup 2 composed of five physical machines and 19 virtual machines and Setup 3 composed of ten physical machines and 28 virtual machines. The performance parameters, namely, QoS, migration cost, load and energy, of the proposed work are compared over the other existing works. The proposed algorithm obtained maximum resource utilization with a good QoS at a rate of 0.19, and minimal migration cost at a rate of 0.015, and minimal energy at a rate of 0.26 with a minimal load at a rate of 0.1551, whereas with the existing methods like ant colony optimization (ACO), gravitational search algorithm (GSA) and exponential gravitational search algorithm, the values of QoS, load, migration cost and energy are 0.16, 0.1863, 0.023 and 0.29; 0.16, 0.1863, 0.023 and 0.28 and 0.18, 0.1657, 0.016 and 0.27, respectively.Originality/valueThis paper presents an algorithm named DEGSA based on VMM strategy to determine the optimum position to perform the VMM to achieve a better load balancing.
Kybernetes – Emerald Publishing
Published: May 24, 2018
Keywords: Energy consumption; Load balancing; Virtual machine migration
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.