Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

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

doi:10.1145/3314148.3314357

Cited by 171 publications

(166 citation statements)

References 29 publications

Supporting

Mentioning

165

Contrasting

Order By: Relevance

“…where Φ l l is a binary variable which is 1 if l is deployed at the substrate link l, r l,bw is the available amount of bandwidth at the substrate link l. Unlike the bandwidth, it is non-trivial to derive accurate models for latency and loss rate [36], especially in multi-hop environment [37]. Consequently, the QoS constraints of latency and loss rate are difficult to be formulated.…”

Section: A Problem Formulationmentioning

confidence: 99%

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Quang

Hadjadj‐Aoul

Outtagarts

2019

IEEE Trans. Netw. Serv. Manage.

121

View full text Add to dashboard Cite

Network Function Virtualization (NFV) and service orchestration simplify the deployment and management of network and telecommunication services. The deployment of these services requires, typically, the allocation of Virtual Network Function -Forwarding Graph (VNF-FG), which implies not only the fulfillment of the service's requirements in terms of Quality of Service (QoS), but also considering the constraints of the underlying infrastructure. This topic has been well-studied in existing literature, however, its complexity and uncertainty of available information unveil challenges for researchers and engineers. In this paper, we explore the potential of reinforcement learning techniques for the placement of VNF-FGs. However, it turns out that even the most well-known learning technique is ineffective in the context of a large-scale action space. In this respect, we propose approaches to find out feasible solutions while improving significantly the exploration of the action space. The simulation results clearly show the effectiveness of the proposed learning approach for this category of problems. Moreover, thanks to the deep learning process, the performance of the proposed approach is improved over time.

show abstract

Section: A Problem Formulationmentioning

confidence: 99%

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Quang

Hadjadj‐Aoul

Outtagarts

2019

IEEE Trans. Netw. Serv. Manage.

121

View full text Add to dashboard Cite

show abstract

“…[23] argues that a network AI can be used to predict future network traffic from past data to evolve network management and automation. Using a network AI, [24], [25] and [26] focus on intelligent traffic routing for aggregated traffic characteristics and improved network analytics. For verification, prediction models can be cross-checked, e.g., with existing evaluations of the interpretability of deep learning models used in the area of computer networks [27].…”

Section: Related Workmentioning

confidence: 99%

Predicting Network Flow Characteristics Using Deep Learning and Real-World Network Traffic

Hardegen

Pfülb

Rieger

et al. 2020

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

We present a processing pipeline for flow-based traffic classification using a machine learning component leveraging Deep Neural Networks (DNNs). The system is trained to predict likely characteristics of real-world traffic flows from a campus network ahead of time, e.g., a flow's throughput or duration. Training and evaluation of DNN models are continuously performed on a flow data stream collected from a university data center. Instead of the common binary classification into "mice" and "elephant" (throughput) or "short-term" and "long-term" (duration) flows, predicted flow characteristics are quantized into three classes. Various communication contexts (subset of network traffic, e.g., only TCP) and flow feature groups (subset of flow features, e.g., only a flow's 5-tuple), which are supported through an enrichment strategy, are considered and investigated. An in-depth description of the data acquisition process, including preprocessing steps and anonymization used to protect sensitive information, is given. Additionally, we employ an accelerated variant of t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize network traffic data. This enables the understanding of traffic characteristics and relations between communication flows at a glance. Furthermore, possible use-cases and a highlevel architecture for flow-based routing scenarios utilizing the developed pipeline are proposed.

show abstract

“…Reinforcement learning tackles optimization problems [14]. GCNs address network-related issues [15], and encoderdecoder architecture is widely used in semantic segmentation and sequence-to-sequence tasks. The GAN is an immensely powerful CNN to learn the statistics of training data and has been widely used to improve the performance of other DL networks in CV [1].…”

Section: B Selecting CV Techniquesmentioning

confidence: 99%

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies,Opportunities, and Challenges

Tian¹,

Pan²,

Alouini³

2020

Preprint

View full text Add to dashboard Cite

<div>Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other fields. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.</div>

show abstract

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

Cited by 171 publications

References 29 publications

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

A Deep Reinforcement Learning Approach for VNF Forwarding Graph Embedding

Predicting Network Flow Characteristics Using Deep Learning and Real-World Network Traffic

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies,Opportunities, and Challenges

Contact Info

Product

Resources

About