Load Balancing Based on Closed Loop Control Theory (LBBCLCT): A Software Defined Networking (SDN) powered server load balancing system based on closed loop control theory

Malavika, R.; Valarmathi, M. L.

doi:10.1002/cpe.6854

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…It facilitates a more holistic view of resource management, prompting a reevaluation of legacy systems and spearheading a transition towards comprehensive, agile, and future-oriented data center ecosystems. Such advancements underscore the pivotal role of SDN in shaping the trajectory of new business development and technological progress within digital infrastructures [46]. Fig.…”

Section: Methodsmentioning

confidence: 99%

Load Balancing in DCN Servers Through Software Defined Network Machine Learning

Beissenova,

Zhidebayeva,

Kopzhassarova

et al. 2024

IJACSA

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In this research paper, we delve into the innovative realm of optimizing load balancing in Data Center Networks (DCNs) by leveraging the capabilities of Software-Defined Networking (SDN) and machine learning algorithms. Traditional DCN architectures face significant challenges in handling unpredictable traffic patterns, leading to bottlenecks, network congestion, and suboptimal utilization of resources. Our study proposes a novel framework that integrates the flexibility and programmability of SDN with the predictive and analytical prowess of machine learning. We employed a multi-layered methodology, initially constructing a virtualized environment to simulate real-world DCN traffic scenarios, followed by the implementation of SDN controllers to instill adaptiveness and programmability. Subsequently, we integrated machine learning models, training them on a substantial dataset encompassing diverse traffic patterns and network conditions. The crux of our approach was the application of these trained models to anticipate network congestion and dynamically adjust traffic flows, ensuring efficient load distribution among servers. A comparative analysis was conducted against prevailing load balancing methods, revealing our model's superiority in terms of latency reduction, enhanced throughput, and improved resource allocation. Furthermore, our research illuminates the potential for machine learning's self-learning mechanism to foresee and adapt to future network states or exigencies, marking a significant advancement from reactive to proactive network management. This convergence of SDN and machine learning, as demonstrated, ushers in a new era of intelligent, scalable, and highly reliable DCNs, demanding further exploration and investment for future-ready data centers.

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Section: Methodsmentioning

confidence: 99%

Load Balancing in DCN Servers Through Software Defined Network Machine Learning

Beissenova,

Zhidebayeva,

Kopzhassarova

et al. 2024

IJACSA

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“…The proposed approach is an extension to Load Balancing Based on the Closed Loop Control Theory (LBBCLT) [15] load balancing approach that uses a dynamic probe generator to probe the servers about the response time and link bandwidth. This article introduces a path selection module in it and the path is selected using the Ant Colony Optimization (ACO).…”

Section: Figure 1: Data Center Fat Tree Topologymentioning

confidence: 99%

“…The authors proposed an algorithm that gets the server response time and then distributes the network traffic according to the server response time [15]. In this algorithm first, measure the flow of the traffic.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The interval generated by this module will trigger all other modules in the proposed approach the path and server performance monitor and traffic accumulator will be immediately triggered, the next interval will be found and after server performance monitoring, the server selector will select the server and the traffic will be redirected using the selected path towards the selected server. This module is inspired by the LBBCLCT [15] approach.…”

Section: I=i ×075 Ifmentioning

confidence: 99%

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SD-ALB: Software Defined Adaptive load balancing in Data Center Network

Fazal,

Gilani,

Khalid

2023

KJCIS

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Network monitoring has crucial importance in data center networks to analyze the behavior of the underlying network. This analysis is used for working on multiple network parameters and load balancing is one of them. This article proposes an adaptive load-balancing approach to balance the load between the servers while changing its behavior with a change in traffic. Software Defined Networking (SDN) provides the single point of network configuration called SDN controller. This approach is facilitating the easy implementation of adaptive load balancing in Data Center Networks. The proposed approach is an extension to the LBBCLT load balancing approach that uses a dynamic probe generator to probe the servers about the response time and link bandwidth. We incorporate a path selection module in it and the path is selected using the Ant Colony Optimization. The results show that the bandwidth consumption and throughput have been improved and servers are receiving the load according to their capacities.

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