Improving Resource Allocation in Software-Defined Networks using Clustering

TL;DR

This paper proposes a clustering-based method using K-mean++ to improve memory resource allocation and load balancing in software-defined networks, aiming to enhance network efficiency and reduce delays.

Contribution

It introduces a novel clustering approach for resource allocation in SDNs, leveraging load distribution to optimize memory use and network performance.

Findings

Load balancing improves network efficiency.

Higher number of clusters increases memory capacity.

Data transmission favors high-quality, low-delay clusters.

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.