A comparative review analysis for load balancing techniques in Cloud Computing using Machine Learning

Load balancing (LB) is a task to manage the performance and efficiency of multi-attribute, multi-variant cloud computing (CC) resources. CC system is more effective when its whole resources are employed in best probable manner and maintaining its load in proper accessing of its resources. Load includes the CPU load, network load, data traffic load, client requests load etc. So, load balancing process includes the management of all these loads as per the availability of resources. This paper deals with the multiple load balancing techniques and present them in hierarchical form and analysis them in performance and efficiency dynamics. This paper also discusses the comparative analysis of performance of existing techniques related to LB in CC using Machine learning (ML). A brief about hybrid methods will also be discussed through this paper. In conclusion part on the basis of analysis done through this study paper also suggests some new insights for LB in CC.

• Publication year

  2022

• Venue

  2022 International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP)

• Publication date

  2022-11-23

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICFIRTP56122.2022.10059412
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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