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Cluster Computing
Published in: Cluster Computing 2/2020

21-09-2019

Improving resource allocation in software-defined networks using clustering

Authors: Mahdi Sarbazi, Mehdi Sadeghzadeh, Seyyed Javad Mir Abedini

Published in: Cluster Computing | Issue 2/2020

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Abstract

Software-defined networks (SDNs) are a huge evolution in simplifying implementation and network operation which have reduced costs and made the network programmable. Although SDNs are a suitable option for solving some of the previous problems, but they have some challenges as any other new technology. Resource allocation and balance control in network is one of the main challenges of this technology which is studied in this paper. In this study, a new approach is proposed for improving memory resource allocation in network using load distribution clusters. Since in the proposed method, K-mean++ algorithm is used for clustering, load balancing of clusters can be used to preserve load balance of the network. In the proposed method, data with higher recall is transmitted to high-quality clusters in terms of average number of hubs and lower average delay between server and user. In the proposed method, by increasing number of clusters, higher memory is created in the network.
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Metadata
Title
Improving resource allocation in software-defined networks using clustering
Authors
Mahdi Sarbazi
Mehdi Sadeghzadeh
Seyyed Javad Mir Abedini
Publication date
21-09-2019
Publisher
Springer US
Published in
Cluster Computing / Issue 2/2020
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-019-02985-3

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