Dynamic Allocation of Cloud Resources Using Skewness and SVM Algorithm

Authors

  • Dr. P. Tamijiselvy  Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
  • Ramprakash M  Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India
  • Aswath R  Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT1952103

Keywords:

Cloud Infrastructure; Big Data; Resource Allocation; Optimization; Automated Resource Allocation.

Abstract

Exploring huge information applications realize huge data and also difficulties to modernize group and so the genius community. Cloud computing with its huge opportunity is the way to deal these issues. Let that be as it is, this cannot play its part on the off beat that we do not expert in fine allocation for cloud foundation resources. In this paper, we introduce a multi-target advancement calculation to exchange off the execution of Big Data and accessibility of Big Data, thereby reducing the cost of application running on Cloud. In the view of splitting and showcasing the interweaved relations among these destinations, we plan and execute our approach on trial condition. At long last, three sets of analyses demonstrate that our approach can keep running our application quicker than other regular improvement techniques and can accomplish the process at an higher execution rate than other heuristic calculations, while also having reduction in the cost of the system due to lesser usage of resources.

References

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Published

2019-04-30

Issue

Section

Research Articles

How to Cite

[1]
Dr. P. Tamijiselvy, Ramprakash M, Aswath R, " Dynamic Allocation of Cloud Resources Using Skewness and SVM Algorithm, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.402-410, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952103