A Hybrid AFSS-SHC Optimization Methodology for Balancing the Load Efficiently in Cloud Environment
Keywords:Cloud Computing, Load Balancing, Swarm intelligence, Virtual Machines and Make span.
Cloud computing helps to share data and provide many resources to users. Users pay only for those resources as much they used. Rapid increase in load to these cloud framework cannot be predicted. Load balancing is one of the issues in cloud computing that distributes the workload to the nodes in such a way no node is overloaded or under - loaded. Load balancing is a main challenge in cloud environment. In this work, scheduling algorithm is applied for load balancing by considering the cost of task execution and make span. This scheduling algorithm efficiently maps task to available nodes in cloud and it is beneficial to user and service provider. Load balancing segregates assignments of tasks among all available virtual machines from datacenters. Assignment of tasks to virtual machines can be done with minimum delay. To enhance the make span, resource utilization, our proposed framework utilizes AFSS-SHC load balancing strategy. A metaheuristics swarm intelligence algorithm which is NP-hard have been suggested to balance load across devices. The algorithms taken into account are-HEFT,PSO and PSO-HC. The proposed methodology AFSS-SHC optimized the task scheduling. Random tasks have been taken for this purpose and simulated to show that the proposed methodology works efficiently to reduce the make span of tasks to reduce the cost.
. R. W. Lucky, 'Cloud computing', IEEE Journal of Spectrum, Vol. 46, No. 5, May 2009.
. Uddalak Chatterjee , 'A Study on Efficient Load Balancing Algorithms in Cloud Computing Environment', International Journal of Current Engineering and Technology, Vol.3, No.5 , December 2013.
. Klaithem , Nader, Mariam and Jameela ,' A Survey of Load Balancing in Cloud Computing :Challenges and Algorithms', 2012 IEEE Second Symposium on Network Cloud Computing and Applications
. Ali M. Alakeel, 'A Guide to Dynamic Load Balancing in Distributed Computer Systems', IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.6, June 2010.
. Sandeep, Sarabjit and Meenakshi, 'Performance Analysis of Load Balancing algorithms' , World Academy of Science, Engineering and Technology 2008.
. Negar and Nima, A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments, 2017 The Korean Institute of Communications Information Sciences.
. M. Ashouraie and N. Jafari Navimipour, 'Priority-based task scheduling on heterogeneous resources in the expert cloud', Kybernetes 44, 1455â€“1471, 2015.
. P. Azad, J.N. Navimipour, An energy-aware task scheduling in cloud computing using a hybrid cultural and ant colony optimization algorithm, Int. J. Cloud Appl. Comput. 7 (2017).
. N.J. Navimipour, F.S. Milani, Task scheduling in the cloud computing based on the cuckoo search algorithm, Int. J. Model. Optim. 5 (2015) 44.
. M. Akbari, H. Rashidi, A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems, Expert Syst. Appl. 60 (2016) 234â€“248.
. S. Padmavathi, S. MohitGolchha, A. SeeniMohamed, Memetic algorithm based task scheduling using probabilistic local search, in: B.K. Panigrahi, S. Das, P.N. Suganthan, P.K. Nanda (Eds.), Swarm, Evolutionary, and Memetic Computing: Third International Conference, SEMCCO 2012, Bhubaneswar, India, December 20â€“22, Proceeding, Springer, Berlin, Heidelberg, 2012, pp. 224â€“231.
. Blackwell and Bentley,' Dynamic Search with Charged Swarms. In: Proceedings of the Genetic and Evolutionary Computation Conference', pp. 19â€“26, 2002.
. Carlisle and Dozier, ' Applying the particle swarm optimizer to non-stationary environments' , Thesis, Auburn University, Australia, 2002.
. H. Topcuoglu, Hariri, Min-You, 'Performance-effective and low complexity task scheduling for heterogeneous computing', IEEE Trans. Parallel Distributed Systems, 260â€“274, 2002.
. Russell and Norvig, 'Artificial intelligence: A modern approach 3/e', Pearson Publication, ISBN-10:136042597, 2010.
. Keshanchi and Jafari Navimipour, 'Priority-based task scheduling on cloud computing environment using a memetic algorithm', Circuits System Computing, 2015.
. Kumari, Raja, Shanthini, 'A hybrid approach of genetic algorithm and multi objective pso task scheduling in cloud computing', Asian J. Res. Soc. Sci. Human, 1260â€“1271, 2017.
View Full Article:
How to Cite
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aÂ Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (SeeÂ The Effect of Open Access).