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Robust Resource Scheduling With Optimized Load Balancing Using Grasshopper Behavior Empowered Intuitionistic Fuzzy Clustering in Cloud Paradigm


Affiliations
1 PG and Research Department of Computer Science, Presidency College, Chennai, Tamil Nadu, India
 

With the advancement in internet technology, everyone can able to utilize resources with low cost using cloud resources. There will be numerous requests for task scheduling to share resources in the cloud environment. When the task request is received by the cloud technology it should have the ability to distribute the workload among sharable resources in a balanced manner and effective utilization of resources. Machine learning and metaheuristic algorithms provide a dynamic part in balanced task assignments in the cloud paradigm. Existing unsupervised models-based load balancing, centroid selection is done randomly and imprecise job requests are not well handled by them. This paper aims to develop a clustering model-based task scheduling with the knowledge of behavioural inspired optimization algorithm in a highly balanced manner. A robust Intuitionistic Fuzzy C-means empowered grasshopper optimization has been anticipated in this work, which utilizes the merits of the Intuitionistic fuzzy and Grass Hopper algorithm for prominent task scheduling among virtual servers in a cloud environment. The results proved that IFCM-GOA reduces the makespan, execution time and, high balance load scheduling with improved cloud resource utilization.

Keywords

Task Scheduling, Cloud Computing, Machine Learning, Intuitionistic Fuzzy C Means, Grasshopper Optimization.
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  • N. Kim, J. Cho and E. Seo, "Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems", Future Generation Computer Systems, vol. 32, pp. 128-137, 2014.

  • S. Singh and I. Chana, "Consistency verification and quality assurance (CVQA) traceability framework for SaaS", 2013 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, pp. 1-6, 2013.

  • Li X. and Zheng M., “An Energy-Saving Load Balancing Method in Cloud Data Centers”, In: Frontier and Future Development of Information Technology in Medicine and Education. Lecture Notes in Electrical Engineering, vol. 269. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7618-0_35.

  • F. Chen, J. Grundy, J.-G. Schneider, Y. Yang and Q. He, "Automated analysis of performance and energy consumption for cloud applications", In Proceedings of the 5th ACM/SPEC international conference on Performance engineering, ACM, pp. 39-50, 2014. https://doi.org/10.1145/2568088.2568093.

  • Tilak, S., and Patil, D., “A survey of various scheduling algorithms in cloud environment”, International Journal of Engineering Inventions, vol. 1, no. 2, 36-39, 2012.

  • K. Pradeep and T. P. Jacob, “Comparative analysis of scheduling and load balancing algorithms in cloud environment”, In: Proc. of International Conf. on Control, Instrumentation, Communication and Computational Technologies, pp. 526-531, 2016.

  • R. Raju, J. Amudhavel, M. Pavithra, S. Anuja and B. Abinaya, "A heuristic fault tolerant MapReduce framework for minimizing makespan in Hybrid Cloud Environment," International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, pp. 1-4, 2014. doi: 10.1109/ICGCCEE.2014.6922462.

  • Ge, Y., & Wei, G., “GA-based task scheduler for the cloud computing systems”, International Conference on Web Information Systems and Mining, WISM 2010), Sanya, vol. 2, pp. 181-186, IEEE, 2010. doi: 10.1109/WISM.2010.87

  • Jang, S. H., Kim, T. Y., Kim, J. K., and Lee, J. S. “The study of genetic algorithm-based task scheduling for cloud computing”, International Journal of Control and Automation, vol. 5, no. 4, pp. 157-162, 2012.

  • Guo Q, “Task scheduling based on ant colony optimization in cloud environment”, In AIP Conference Proceedings, vol. 1834, no. 1, p. 040039, 2017.

  • Zuo, L., Shu, L., Dong, S., Zhu, C., and Hara, T.,” A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing”, pp. 2687-2699, 2015. doi: 10.1109/ACCESS.2015.2508940.

  • H. Liu, A. Abraham, A.E. Hassanien, “Scheduling Jobs on computational grids using a fuzzy particle swarm optimization algorithm”, Future Generation Computer Systems, 2009.

  • Ch.Srinivasa Rao, B. Raveendra Babu, “DE Based Job Scheduling in Grid Environments”, Journal of Computer Networks, vol. 1, no. 2, pp. 28-31, 2013.

  • Juan, W., Fei, L., and Aidong, C., “An Improved PSO based Task Scheduling Algorithm for Cloud Storage System”, Advances in Information Sciences and Service Sciences, vol. 4, no. 18, pp. 465-471, 2012.

  • Krishnasamy K., ”Task Scheduling Algorithm Based on Hybrid Particle Swarm Optimization In Cloud Computing Environment”, Journal of Theoretical and Applied Information Technology, vol. 55, no.1 , pp. 33-38, 2013.

  • Alkayal, E. S., Jennings, N. R., and Abulkhair, M. F.,”Efficient task scheduling multi-objective particle swarm optimization in cloud computing”, In 2016 IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops). pp. 17-24, IEEE, 2016. doi:10.1109/LCN.2016.024

  • Rao, R. V., Savsani, V. J., Vakharia, D. P, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems”, Computer-Aided Design, vol. 43, no. 3, pp. 303-315, 2011.

  • Dipesh Pradhan, Feroz Zahid, “Data Center Clustering for Geographically Distributed Cloud Deployments, Primate Life Histories, Sex Roles, and Adaptability”, pp. 1030-1040, 2018. doi: 10.1007/978-3-030-15035-8_101.

  • Amer Al-Rahayfeh , Saleh Atiewi , Abdullah Abuhussein, MuderAlmiani,”Novel Approach to Task Scheduling and LoadBalancing Using the Dominant Sequence Clusteringand Mean Shift Clustering Algorithms”, Future Internet, vol. 11,no. 109 , pp 1-15, 2019.

  • Malinen M.I., FräntiP. , “Balanced K-Means for Clustering. In: Fränti P., Brown G., Loog M., Escolano F., Pelillo M. (eds) Structural, Syntactic, and Statistical Pattern Recognition”, Lecture Notes in Computer Science, vol 8621. Springer, Berlin, Heidelberg, 2014.

  • Geetha Megharaj, Dr. Mohan G. Kabadi, Rajani, Deepa M, “FCM-LB: Fuzzy C Means Cluster Based Load Balancing in Cloud”, International Journal of Innovative Research in Science, Engineering and Technology, vol. 7, Special Issue 6, 2018.

  • Atanassov K, “Intuitionistic fuzzy logics as tools for evaluation of data mining processes”, Knowl-Based Syst, vol. 80, pp. 122–130, 2015. doi:10.1016/j.knosys.2015.01.015.

  • Zeshui Xu, and Junjie Wu,”Intuitionistic fuzzy C-means clustering algorithms”, Journal of Systems Engineering and Electronics, vol. 21, no. 4, pp.580–590, 2010. doi:10.3969/j.issn.1004-4132.2010.04.009.

  • Shahrzad Saremi, Seyedali Mirjalili, Andrew Lewis, “Grasshopper Optimisation Algorithm: Theory and application”, Advances in Engineering Software, vol. 105, pp. 30-47, 2017.


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  • Robust Resource Scheduling With Optimized Load Balancing Using Grasshopper Behavior Empowered Intuitionistic Fuzzy Clustering in Cloud Paradigm

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Authors

G. Kiruthiga
PG and Research Department of Computer Science, Presidency College, Chennai, Tamil Nadu, India
S. Mary Vennila
PG and Research Department of Computer Science, Presidency College, Chennai, Tamil Nadu, India

Abstract


With the advancement in internet technology, everyone can able to utilize resources with low cost using cloud resources. There will be numerous requests for task scheduling to share resources in the cloud environment. When the task request is received by the cloud technology it should have the ability to distribute the workload among sharable resources in a balanced manner and effective utilization of resources. Machine learning and metaheuristic algorithms provide a dynamic part in balanced task assignments in the cloud paradigm. Existing unsupervised models-based load balancing, centroid selection is done randomly and imprecise job requests are not well handled by them. This paper aims to develop a clustering model-based task scheduling with the knowledge of behavioural inspired optimization algorithm in a highly balanced manner. A robust Intuitionistic Fuzzy C-means empowered grasshopper optimization has been anticipated in this work, which utilizes the merits of the Intuitionistic fuzzy and Grass Hopper algorithm for prominent task scheduling among virtual servers in a cloud environment. The results proved that IFCM-GOA reduces the makespan, execution time and, high balance load scheduling with improved cloud resource utilization.

Keywords


Task Scheduling, Cloud Computing, Machine Learning, Intuitionistic Fuzzy C Means, Grasshopper Optimization.

References





DOI: https://doi.org/10.22247/ijcna%2F2020%2F203851