Load Balancing on Software Defined Networks

Babayiğit, Bilal; Ulu, Banu

doi:10.1109/ismsit.2018.8567070

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…It can also be the reason to halt the network completely. The load balancing method that was suggested by Babayigit et al [33] distributes the network's workload among several sources and prevents resource overloading. After describing the load balancing issue in SDN, two distinct load balancing algorithms (Round Robin and Dijkstra) are tested to see which performs better.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The suggested methodology streamlines all transactions using Ethereum, a platform built on a blockchain and supporting Proof of Authority (PoA) consensus. If we compare our proposed solution with Babayigit et al [33], Chen et al [34], filali et al [35] and Anita et al [36], then all these have used traditional methods for load balancing. There is no one that explained on what basis they are doing load balancing between different services.…”

Section: Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

An Intelligent Load Balancing Technique for Software Defined Networking Based 5G Using Machine Learning Models

Almakdi,

Aqdus,

Amin

et al. 2023

IEEE Access

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The emergence of two new technologies, namely software defined networking (SDN) and 5G networks, has greatly changed the development of network functions and network topologies. These two technologies provide cost benefits for mobile operators, a more flexible and scalable network, and a shorter time to market for new services and applications. Scalability and effectiveness are increased when 5G and SDN are used together. SDN increases the reliability of the 5G network by separating the control plane from the data plane. Incorrect load balancing, a lack of knowledge of network traffic, and other issues make it difficult to provide Quality of Service (QoS) with SDN. This research proposes a unique loadbalancing method to resolve these concerns using Hierarchical Agglomerative Clustering (HAC) and Back Propagation Neural Network (BPNN) algorithms. The proposed method segments the network services into several groups after normalizing data requirements. It consists of two phases: in first phase, there is clustering of bandwidth for different services (e.g., social media, automated homes, and automated cars) inside the network for different data requirements. The agglomerative hierarchical clustering (single link technique) is implemented to make the clusters of bandwidth inside the SDN work based on minimum distance. After clustering, we allotted bandwidths to the respective clusters. In the second phase, the BPNN technique trains the network to choose the optimal path and check the error faults. The proposed approach evaluates the network delay, packet loss, throughput, latency rate, and bandwidth usage to evaluate the performance with Multiple Regression-based Searching (MRBS) and Software-defined Sensor Network Load Balancing (SDSNLB) algorithms. The experimental results of the proposed model are promising as the performance increased by 15%, 23%, 27%, 21%, and 30%, respectively, compared with existing approaches. In addition we compared the computational time complexity with increased the no of nodes and services, when the rate of nodes and services is varying, the proposed solution's efficiency remains constant.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

An Intelligent Load Balancing Technique for Software Defined Networking Based 5G Using Machine Learning Models

Almakdi,

Aqdus,

Amin

et al. 2023

IEEE Access

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“…With the SDN architecture, in contrast to the traditional network architecture, the control plane is separated from the data plane which enables to management and programs of the network easily. In SDN, the data layer is the infrastructure layer and consists of switches and routers, while the control layer is the brain of the network and responsible for leading the network centrally, 26 also it manages the network by communicating with the data and application layers. The application layer has applications that can change the network structure by providing remote access to the controller via the northbound (Rest‐API).…”

Section: Preliminarymentioning

confidence: 99%

Deep learning for load balancing of SDN‐based data center networks

Babayiğit

Ulu

2021

Int J Communication

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Summary With the development of new communication technologies, the amount of data transmission has increased gradually. To satisfy this increasing computing resource demand effectively, the number of data center networks (DCNs), which are structures composed of servers connected with well‐organized‐switches, has increased worldwide. However, traditional switches do not efficiently satisfy the needs of DCNs. In recent years, an emerging networking architecture software‐defined network (SDN) has been proposed to manage the DCNs to control network switches and to deploy new network protocols. However, the main challenge in DCNs is to balance the load among servers. One potential solution for this challenge is to use machine learning (ML) techniques to tackle network transmission demand. A recent successful ML technique is deep learning (DL) which makes prediction, classification, and decisions by handling large amounts of data. Although DL has drawn increasing attention in many research fields, its applications to networking problems are scarce. In this paper, a DL technique is proposed for the load‐balancing of SDN‐based DCNs. To train the DL network, the variable load values among links are used. The response time for load balancing of the DL technique is compared with those of different ML algorithms, such as an artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). The experimental results reveal that the response‐time results of ANN and DL are lower than those of the SVM and LR algorithms. Also, DL accuracy is higher than ANN accuracy. As a result, DL is very efficient for the load balancing of SDN‐based DCNs.

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“…CPP has been intensely studied in the literature. Papers [4][5][6][7][8][9][10][11][12][13][14][15][16] explain the different CPP problems in detail for the proper single controller or multiple distributions of controllers and clarify the benefits of SDN.…”

Section: Related Workmentioning

confidence: 99%

“…In [12], the authors implement a small scale and low-cost SDN design. The performance of two different load balancing algorithms (Round Robin and Dijkstra) is investigated in [13] using round-trip times for addressing the SDN load balancing problem.…”

Section: Related Workmentioning

confidence: 99%