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Vers un Management basé ML des Réseaux SDNs

Kokouvi Benoit Nougnanke 1
1 LAAS-SARA - Équipe Services et Architectures pour Réseaux Avancés
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : With the exponential growth in technology performance, the modern world has become highly connected, digitized, and diverse. Within this hyper-connected world, Communication networks or the Internet are part of our daily life and play many important roles. However, the ever-growing internet services, application, and massive traffic growth complexify networks that reach a point where traditional management functions mainly govern by human operations fail to keep the network operational. In this context, Software-Defined Networking (SDN) emerge as a new architecture for network management. It makes networks programmable by bringing flexibility in their control and management. Even if network management is eased, it is still tricky to handle due to the continuous growth of network complexity. Management tasks remain then complex. Faced with this, the concept of self-driving networking arose. It consists of leveraging recent technological advancements and scientific innovation in Artificial Intelligence (AI)/Machine Learning (ML) with SDN. Compared to traditional management approaches using only analytic mathematical models and optimization, this new paradigm is a data-driven approach. The management operations will leverage the ML ability to exploit hidden pattern in data to create knowledge. This association SDN-AI/ML, with the promise to simplify network management, needs many challenges to be addresses. Self-driving networking or full network automation is the “Holy Grail” of this association. In this thesis, two of the concerned challenges retain our attention. Firstly, efficient data collection with SDN, especially real-time telemetry. For this challenge, we propose COCO for COnfidence-based COllection, a low overhead near-real-time data collection in SDN. Data of interest is collected efficiently from the data plane to the control plane, where they are used whether by traditional management applications or machine-learning-based algorithms. Secondly, we tackle the effectiveness of the use of machine learning to handle complex management tasks. We consider application performance optimization in data centers. We propose a machine-learning-based incast performance inference, where analytical models struggle to provide general and expert-knowledge-free performance models. With this MLperformance model, smart buffering schemes or other QoS optimization algorithms could dynamically optimize traffic performance. These ML-based management schemes are built upon SDN, leveraging its centralized global view, telemetry capabilities, and management flexibility. The effectiveness of our efficient data collection framework and the machine-learningbased performance optimization show promising results. We expect that improved SDN monitoring with AI/ML analytics capabilities can considerably augment network management and make a big step in the self-driving network journey.
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Submitted on : Friday, July 30, 2021 - 11:47:13 AM
Last modification on : Tuesday, October 19, 2021 - 11:18:03 PM

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Kokouvi Benoit Nougnanke. Vers un Management basé ML des Réseaux SDNs. Networking and Internet Architecture [cs.NI]. Université Toulouse 3 - Paul Sabatier, 2021. English. ⟨tel-03343066v1⟩

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