Load Balancing model based on Machine Learning and Segment Routing in SDN

Todorov, Dimitar; Valchanov, Hristo; Aleksieva, Veneta

doi:10.1109/icai50593.2020.9311385

Cited by 13 publications

(2 citation statements)

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“…Finally, Todorov et al [135] present an architectural design to implement four types of ML techniques to improve load balancing and segment routing in SDN. However, the article does not provide any additional insights on implementation nor provides any type of evaluation.…”

Section: A Supervised 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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Section: A Supervised Learningmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Routing Optimization in SDN

et al. 2021

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“…Research and development must continue to focus on making sure these potent instruments are used responsibly and diligently. There [10] is a wide range of related work being done at the intersection of machine learning and applied nonlinear analysis in communication technology. Combining these fields continues to spur innovation and influence the direction of communication technology, from basic research in nonlinear dynamics to useful applications in network optimisation, cybersecurity, and predictive modelling.…”

Section: Introductionmentioning

confidence: 99%

Exploring Applied Nonlinear Analysis and Machine Learning in the Evolution of Communication Technology

Sunil M Sangve

2023

cana

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The combination of machine learning and applied nonlinear analysis has greatly aided the advancement of communication technologies. This collaboration has transformed the industry and sped up developments across a number of fields. Because of its emphasis on complex system dynamics, nonlinear analysis has contributed to a deep understanding of complex communication behaviours, which has made it possible to create systems that are more reliable and effective. Concurrently, machine learning methodologies have enhanced this domain by permitting the handling of extensive datasets and the identification of complex patterns, hence optimising network efficiency and flexibility. The fusion of these fields has produced ground-breaking discoveries. The use of machine learning and nonlinear analysis has improved signal processing in telecommunications and data transmission, enabling more dependable and rapid data transport. Furthermore, by enabling the development of adaptive and self-organizing networks, these technologies have improved network coverage and signal propagation in wireless communication. By facilitating the identification of intricate patterns in communication data, this convergence has also accelerated improvements in cybersecurity by strengthening networks against ever-evolving attacks. Moreover, predictive modelling in communication systems has been improved by the use of machine learning algorithms in nonlinear analysis. These methods provide insights into future network behaviour and support proactive system management. The combination of machine learning and applied nonlinear analysis is a fundamental aspect of the development of communication technology. The interaction of these domains has improved communication systems' dependability and efficiency while also opening doors for creative thinking and fostering the ongoing development of this vital area of technology.

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