Machine Learning to Balance Web Applications using SDN

Authors: Prakruthi M Krishna, Sanjana S Rao, Simran S Bhurat, Tata Venkatesh, Prof Vani K A

Country: India

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Abstract: Using the consistent hash functions, core of an existing load balancing mechanism will be static, which is actually expected to be able to map both the servers and client workloads to a hash circle, uniformly. But choosing a Load Balancing algorithm statically does not work in many situations when the applications have unknown behavior or dynamic one which is very common in this world. Traditional networks use hardware components which are expensive, complicated to deploy and require human intervention to work consistently. These limitations make load balancing among servers, complex, expensive and non-scalable. Based on the principles of SDN, we tackle these challenges using an approach that checks and also collects the information about the application requests which are incoming dynamically, and finally according to this analyzed data the model makes a decision of selecting the most suitable Load Balancing algorithm.

Keywords: Load Balancing Algorithm, Dynamic Load Balancer

Paper Id: 1044

Published On: 2021-06-23

Published In: Volume 9, Issue 3, May-June 2021

Cite This: Machine Learning to Balance Web Applications using SDN - Prakruthi M Krishna, Sanjana S Rao, Simran S Bhurat, Tata Venkatesh, Prof Vani K A - IJIRMPS Volume 9, Issue 3, May-June 2021.

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