Server Load Balancing Through Quantum Radial Basis Function Neural Network Algorithm

Server Load Balancing Through Quantum Radial Basis Function Neural Network Algorithm

Authors

  • Ankitha JD
  • Jocely Joseph

Keywords:

radial basis function neural network, quantum computing, superposition, entanglement, qubits

Abstract

This paper is all about the implementation of
quantum radial basis neural network algorithm to increase
the speed of server load balancing. The amount of data
reaching the servers or the load of each servers is very much
high and uncontrollable by itself. The servers load must be
transferred to different servers to reduce the traffic of data in
each server. The data load in server is transferred to the other
that are in the same cluster. The server cluster is a group of
servers connected together to give high availability of data for
the users. There should be some kind of algorithm that could
identify the increasing loads in each server and transfer the
data to different servers. Here I have used Quantum RBF
neural network as the load balancer which acts as traffic
police in maintaining the traffic, that would transfer server
load between different servers within seconds. Since the
algorithm used here is a machine learning algorithm the
QRBF load balancer is trained for learning from the situation
and act accordingly to the specific techniques already
implemented into the QRBF load balancer. Normally a neural
network may take so much of time for predicting or finding
out the result but if the quantum computing technique is used
then the process of load balancing done by QRBF load
balancer will be done in a fraction of seconds, because of the
superposition and entanglement properties of quantum
computing. This work will be very useful for the people who
work on server load balancing.

Published

2022-12-20

How to Cite

Ankitha JD, & Jocely Joseph. (2022). Server Load Balancing Through Quantum Radial Basis Function Neural Network Algorithm. National Conference on Emerging Computer Applications, 3(1). Retrieved from https://ajcejournal.in/nceca/article/view/90
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