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

Babayiğit, Bilal; Ulu, Banu

doi:10.1002/dac.4760

Cited by 11 publications

(2 citation statements)

References 31 publications

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“…11 In public internet, the users and network service providers are fit into diverse domains, so the full information about the network interior states are not observable. 12 Here, the virtualization can be used for offering infrastructure as a resource, and the applications can be given as a virtual machines. Moreover, the number of servers that are executed simultaneously can be reduced for saving energy.…”

Section: Is Specificallymentioning

confidence: 99%

An enhanced HFPA‐IHS‐ORP‐based optimization mechanism for efficient bandwidth allocation in software‐defined networks

Paulraj¹,

Rajaram

2022

Int J Communication

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Summary Nowadays, due to the increased amount of network users and resources, some emerging technologies like big data and cloud have been developed. But managing these types of networks is more complex; in order to solve this issue, a software‐defined network (SDN) is developed for managing the network in a comfortable manner, in which maintaining load balance and energy efficiency of network is highly important to satisfy the requirements of quality of service (QoS). For these reasons, various optimization‐based algorithms are developed in the traditional works, but these lack with the limitations of increased network traffic and energy consumption. In order to solve these issues, an enhanced hybrid flower pollination algorithm (HFPA) with improved harmony search (IHS) is developed in this paper. It intends to obtain an improved resource utilization and reduced network load and delay by efficiently allocating the bandwidth to the network. Moreover, an optimal routing protocol (ORP) is employed to perform the data transmission with condensed network traffic. During experimentation, the performance results of the proposed algorithm are examined and compared with some traditional methodologies. From the results, it is noted that the proposed HFPA‐IHS‐ORP provides the better results than the existing methods.

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Section: Is Specificallymentioning

confidence: 99%

An enhanced HFPA‐IHS‐ORP‐based optimization mechanism for efficient bandwidth allocation in software‐defined networks

Paulraj¹,

Rajaram

2022

Int J Communication

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“…Babayigit et al [180] focus on DCNs and evaluate and compare a DRL technique with others like ANN, SVM and logistic regression. The results show that their approach is very efficient for load balancing, outperforming all the rest in diverse evaluated parameters.…”

Section: ) Deep Reinforcement Learningmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Routing Optimization in SDN

et al. 2021

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In conventional networks, there was a tight bond between the control plane and the data plane. The introduction of Software-Defined Networking (SDN) separated these planes, and provided additional features and tools to solve some of the problems of traditional network (i.e., latency, consistency, efficiency). SDN is a flexible networking paradigm that boosts network control, programmability and automation. It proffers many benefits in many areas, including routing. More specifically, for efficiently organizing, managing and optimizing routing in networks, some intelligence is required, and SDN offers the possibility to easily integrate it. To this purpose, many researchers implemented different machine learning (ML) techniques to enhance SDN routing applications. This article surveys the use of ML techniques for routing optimization in SDN based on three core categories (i.e. supervised learning, unsupervised learning, and reinforcement learning). The main contributions of this survey are threefold. Firstly, it presents detailed summary tables related to these studies and their comparison is also discussed, including a summary of the best works according to our analysis. Secondly, it summarizes the main findings, best works and missing aspects, and it includes a quick guideline to choose the best ML technique in this field (based on available resources and objectives). Finally, it provides specific future research directions divided into six sections to conclude the survey. Our conclusion is that there is a huge trend to use intelligence-based routing in programmable networks, particularly during the last three years, but a lot of effort is still required to achieve comprehensive comparisons and synergies of approaches, meaningful evaluations based on open datasets and topologies, and detailed practical implementations (following recent standards) that could be adopted by industry. In summary, future efforts should be focused on reproducible research rather than on new isolated ideas. Otherwise, most of these applications will be barely implemented in practice.

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Intelligent routing using convolutional neural network in software-defined data center network

Modi

Swain

2022

J Supercomput

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

Cited by 11 publications

References 31 publications

An enhanced HFPA‐IHS‐ORP‐based optimization mechanism for efficient bandwidth allocation in software‐defined networks

An enhanced HFPA‐IHS‐ORP‐based optimization mechanism for efficient bandwidth allocation in software‐defined networks

A Survey on Machine Learning Techniques for Routing Optimization in SDN

Intelligent routing using convolutional neural network in software-defined data center network

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