A MODEL BASED PREDICTION ON LOAD BALANCING USING MACHINE LEARNING ALGORITHMS

Authors

  • Vidyullatha P Associate Professor, Department of Computer Science and Engineering Author
  • Nikhat Parveen Associate Professor, Department of Computer Science and Engineering Author
  • U.Vishnu Priya Associate Professor, Department of Computer Science and Engineering Author
  • M Venkatesh B.Tech Students, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India. Author
  • MVBT Santhi Associate Professor, Department of Computer Science and Engineering Author

DOI:

https://doi.org/10.61841/m5q6mm67

Keywords:

Load balancing, performance, clustering, stability, network, parameters, SVM

Abstract

The Parallel and distributed systems focus on the concept of load balancing. A tangle is split into a hard and fast number of processors that are to be executed in parallel. However, there may be a state that some processors will complete their tasks before other process and reach idle state as the work is unevenly divided or perhaps some processors complete before the others. Ideally, we like all the processors to have the minimum wait time. Achieving the above goal by spreading the tasks is eventually named as load balancing. In the paper, we stress on clustering techniques. The basis of the join-idle-queue algorithm is seen by using clustering. The technique of load balancing uses Support Vector Machine (SVM) and clustering techniques (K-means, Hierarchical). A comparative study of the above techniques is done by means of load balancing

Downloads

Download data is not yet available.

References

[1] Mandal, A. and Chandra, S. (2010) An Empirical Study and Analysis of the Dynamic Load Balancing Algorithms Used in Parallel Computing Systems: Proceedings of ICCS-2010,19-20 Nov. West Bengal: University of North Bengal.

[2] Krishnamoorthy, S., Sadayappan P., Nieplocha P., and Krishnan M. (2005) Locality-aware Load Balancing for Dynamic and Irregular Computations.1st Workshop on Patterns in High-Performance Computing. Urbana-Champaign

[3] Pearce, O., Gamblin, T., Supinskiy, B., Schulzy, M., and Amato, M. (2012) Quantifying the Effectiveness of Load Balance Algorithms.US: Department of Energy

[4] Aldasht, M., Ortega, J. und Puntonet, C. (2007) Dynamic Load Balancing in Heterogeneous Clusters: Exploitation of the Processing Power. 2nd Palestinian International Conference on Computer and Information Technology (PICCIT), Hebron, Palestine.

[5] LeMair W.H. and Reeves P. A. (1993) Strategies for Dynamic Load Balancing on Highly Parallel computers: IEEE Transactions on Parallel and Distributed Systems. Vol. 4, No. 9.

[6] Pandey, S. K., and Tiwari, R. (2013). The Efficient load balancing in the parallel computer: International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 4, April 2013.

[7]. Bhargava, M.G, Vidyullatha, P, Venkateswara Rao p, Sucharita, V," study on potential of big visual data analytics in construction arena",International Journal of Engineering and Technology(UAE)Volume 7, 2018, Pages 652-656.

[8]T.Ganesan, Pothuraju RajaRajeswari, “Genetic Algorithm Based Optimization to Improve the Cluster Lifetime by Optimal Sensor Placement in WSN’s”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) , ISSN: 2278-3075, Volume-8 Issue-8, June, 2019

[9] Krishna, C.T.S.M., Vidyullatha, P, “Enhanced private preservative multimedia sharing data relying on collective finger prints”, International Journal of Scientific and Technology Research, 2020.

[10]. Parveen, N., Roy, A., Sai Sandesh, D., Sai Srinivasulu, J.Y.P.R., Srikanth, N, “Human computer interaction through hand gesture recognition technology”, International Journal of Scientific and Technology Research, 2020.

Downloads

Published

31.10.2020

Issue

Vol. 24 No. 8 (2020): 24 (8) 2020

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

How to Cite

Vidyullatha P, Parveen, N., Priya, U., M Venkatesh, & Santhi, M. (2020). A MODEL BASED PREDICTION ON LOAD BALANCING USING MACHINE LEARNING ALGORITHMS. International Journal of Psychosocial Rehabilitation, 24(8), 12550-12556. https://doi.org/10.61841/m5q6mm67