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Binomial logit regression and centralised agent stochastic optimisation for privacy preserved load balancing in cloud

Author

Listed:
  • M. Jawahar
  • A. Sabari
  • S. Monika

Abstract

Load balancing and privacy preservation of data plays an important role in cloud. Few research works have been designed to perform load balancing on cloud server but, performance was not improved. In order to overcome such limitation, a binomial logit privacy preserved load balancing (BLPPLB) technique is proposed. At first, the request is sent from user to cloud server. BLPPLB Technique carried out binomial logit authentication based on user behaviour on cloud. Then, BLPPLB technique finds the intruder attacks and authorised users in cloud. During data accessing process, load balancing is performed through selecting optimal server among multiple servers for each user requests based on objective function using centralised agent based stochastic local search to provide the requested services. The experimental result shows that the BLPPLB technique is able to increase the load balancing efficiency with higher data confidentiality when compared to state-of-the-art works.

Suggested Citation

  • M. Jawahar & A. Sabari & S. Monika, 2019. "Binomial logit regression and centralised agent stochastic optimisation for privacy preserved load balancing in cloud," International Journal of Information Systems and Change Management, Inderscience Enterprises Ltd, vol. 11(1), pages 25-43.
    • Handle: RePEc:ids:ijiscm:v:11:y:2019:i:1:p:25-43
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