RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN

Rusek, Krzysztof; Suárez-Varela, José; Almasan, Paul; Barlet‐Ros, Pere; Cabellos‐Aparicio, Albert

doi:10.1109/jsac.2020.3000405

Cited by 226 publications

(134 citation statements)

References 20 publications

Supporting

Mentioning

133

Contrasting

Order By: Relevance

“…The work in [28] proposes RouteNet that leverages the ability of Graph Neural Networks (GNN) for network modeling and optimization in SDN. Taking as input network topology information, routing schemes, and traffic matrix RouteNet, based on Generalized Linear Models, can provide accurate source-destination KPIs such as delay distribution (mean delay and jitter) and packet drop prediction.…”

Section: B Machine Learning For Qoe / Qos Inference In Sdnmentioning

confidence: 99%

Learning-based Incast Performance Inference in Software-Defined Data Centers

Nougnanke

Labit

Bruyère

et al. 2021

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

View full text Add to dashboard Cite

Incast traffic is a many-to-one communication pattern used in many applications, including distributed storage, web-search with partition/aggregation design pattern, and MapReduce, commonly in data centers. It is generally composed of short-lived flows that may be queued behind large flows' packets in congested switches where performance degradation is observed. Smart buffering at the switch level is sensed to mitigate this issue by automatically and dynamically adapting to traffic conditions changes in the highly dynamic data center environment. But for this dynamic and smart buffer management to become effectively beneficial for all the traffic, and especially for incast the most critical one, incast performance models that provide insights on how various factors affect it are needed. The literature lacks these types of models. The existing ones are analytical models, which are either tightly coupled with a particular protocol version or specific to certain empirical data. Motivated by this observation, we propose a machine-learning-based incast performance inference. With this prediction capability, smart buffering scheme or other QoS optimization algorithms could anticipate and efficiently optimize system parameters adjustment to achieve optimal performance. Since applying machine learning to networks managed in a distributed fashion is hard, the prediction mechanism will be deployed on an SDN control plane. We could then take advantage of SDN's centralized global view, its telemetry capabilities, and its management flexibility.

show abstract

Section: B Machine Learning For Qoe / Qos Inference In Sdnmentioning

confidence: 99%

Learning-based Incast Performance Inference in Software-Defined Data Centers

Nougnanke

Labit

Bruyère

et al. 2021

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

View full text Add to dashboard Cite

show abstract

“…Rusek et al propose RouteNet, a novel network model based on Graph Neural Networks (GNNs) [4]. This model reflects the complex relationship between network topology, routing, and input traffic to produce estimates of packet delay and loss distributions per source-destination pair.…”

Section: The Selected Papersmentioning

confidence: 99%

“…Several works included in this Special Issue were conducted within the context of softwarization, i.e., software-defined networking (SDN), Virtual Network Functions (VNFs), and so on [3], [4], [13]. The core contribution of some papers, such as [2], lies in theoretical analysis and simulation, while others put strong emphasis on experimentation and evaluation involving many data sets [6].…”

Section: The Selected Papersmentioning

confidence: 99%

“…Some papers use methods based on Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) [5], [8]. One work uses a Graph Neural Network (GNN) to reflect relationships in networked systems [4], and one uses Generative Adversarial Networks (GANs) to address privacy issues [16].…”

Section: The Selected Papersmentioning

confidence: 99%

“…The authors of [9] use the concept of life-long learning as part of their approach. The paper [4] proposes a meta-learning scheme that allows to switch among sub-policies. Finally, [16] applies federated learning to build a solution for user privacy in case of malicious server.…”

Section: The Selected Papersmentioning

confidence: 99%

See 2 more Smart Citations

Guest Editorial Special Issue on Advances in Artificial Intelligence and Machine Learning for Networking

Chemouil

Hui

Kellerer

et al. 2020

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

I. INTRODUCTION A RTIFICIAL Intelligence (AI) and Machine Learning (ML) approaches have emerged in the networking domain with great expectation. They can be broadly divided into AI/ML techniques for network engineering and management, network designs for AI/ML applications, and system concepts. AI/ML techniques for networking and management improve the way we address networking. They support efficient, rapid, and trustworthy engineering, operations, and management. As such, they meet the current interest in softwarization and network programmability that fuels the need for improved network automation in agile infrastructures, including edge and fog environments. Network design and optimization for AI/ML applications addresses the complementary topic of supporting AI/ML-based systems through novel networking techniques, including new architectures and algorithms. The third topic area is system implementation and open-source software development. This evolution draws particular attention to interdisciplinary approaches. Researchers in communication networks apply ML and AI concepts to optimize and automate network architecture, control, and management. Similarly, AI experts collaborate with networking researchers to optimize network support for architecture and design of data communication and processing for AI purposes. This special issue is a follow-up to the JSAC's Special Issue on Artificial Intelligence and Machine Learning for Networking and Communications published in June 2019 [1]. It has been organized by the same core team of researchers.

show abstract

Software defined network‐based multipath state‐aware routing with traffic prediction in satellite network

Luo,

Ning,

Chen

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Uneven traffic demand in satellite networks can lead to link congestion and influence network communication quality. To tackle this problem, we propose a state‐aware routing algorithm based on traffic prediction to disperse network traffic. Reducing the onboard processing requirements, the routing algorithm in this paper introduces a software‐defined network to decouple the control plane and data plane of the satellite network. Also, to better sense the trend of network state, the routing algorithm devises a Spatial‐Temporal Attention Fusion Graph Neural Network (STAFGNN) to predict the future network state by capturing the spatial‐temporal correlation of satellite network traffic. In the control plane, the routing algorithm senses the network state by predicting the future satellite traffic and aggregates the real‐time states of neighboring satellites to identify the load level of the zone. The data plane forwards data based on the control information. Based on the routing algorithm in this paper, multipath routing is designed to reduce the probability of link congestion by dispersing each end‐to‐end traffic. The simulation results demonstrate that the routing algorithm in this paper reduces the number of congestion links and balances the load distribution. The proposed algorithm provides an idea to introduce deep learning to solve the load balancing problem in satellite networks.

show abstract

RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN

Cited by 226 publications

References 20 publications

Learning-based Incast Performance Inference in Software-Defined Data Centers

Learning-based Incast Performance Inference in Software-Defined Data Centers

Guest Editorial Special Issue on Advances in Artificial Intelligence and Machine Learning for Networking

Software defined network‐based multipath state‐aware routing with traffic prediction in satellite network

Contact Info

Product

Resources

About