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Arabian Journal for Science and Engineering

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10-07-2021 | Research Article-Computer Engineering and Computer Science

Load Balancing in DCN Servers through SDN Machine Learning Algorithm

Authors: G. Sulthana Begam, M. Sangeetha, N. R. Shanker

Published in: Arabian Journal for Science and Engineering | Issue 2/2022

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Abstract

Development in Internet technologies increases Internet users exponentially. Increase in users leads to more data center network (DCN) and heavy data traffic in servers. Data traffic in servers is managed through software-defined networking (SDN). SDN improves utilisation of large-scale network resource and performance of network applications. In SDN, load balancing technique optimises the data flow during transmission through server load deviation after evaluating the network status dynamically. However, load deviation in network needs optimum server selection and routing path with respect to less time and complexity. In this paper, we proposed a multiple regression-based searching (MRBS) algorithm for optimum server selection and routing path in DCN to improve performance even under heavy load conditions such as message spikes, different message frequencies, and unpredictable traffic patterns. MRBS selects the server based on regression analysis, which predicts types of traffic and response time based on the server data parameters such as load, response time, and bandwidth and server utilisation. MRBS combines heuristic algorithm and regression model for efficient server and path selection. The proposed algorithm reduces the delay and time more than 45% and shows better sever utilisation of 83% when compared with traditional algorithms due to stochastic gradient decent weights estimation.

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Title: Load Balancing in DCN Servers through SDN Machine Learning Algorithm
Authors: G. Sulthana Begam
M. Sangeetha
N. R. Shanker
Publication date: 10-07-2021
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 2/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-05911-1

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