Understanding the Modeling of Computer Network Delays using Neural Networks

Mestres, Albert; Alarcón, Eduard; Ji, Yusheng; Cabellos‐Aparicio, Albert

doi:10.1145/3229607.3229613

Cited by 55 publications

(40 citation statements)

References 18 publications

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“…Initial attempts to instantiate this idea use fully-connected neural networks (e.g. [18], [19]). Such early attempts do not generalize to networks not seen in training, are not tested with realistic traffic models, and do not model queues.…”

Section: Related Workmentioning

confidence: 99%

Scaling Graph-based Deep Learning models to larger networks

Ferriol-Galmés,

Suárez-Varela,

Rusek

et al. 2021

Preprint

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Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management. Early works using GNN have demonstrated an unprecedented capability to learn from different network characteristics that are fundamentally represented as graphs, such as the topology, the routing configuration, or the traffic that flows along a series of nodes in the network. In contrast to previous solutions based on Machine Learning (ML), GNN enables to produce accurate predictions even in other networks unseen during the training phase. Nowadays, GNN is a hot topic in the Machine Learning field and, as such, we are witnessing great efforts to leverage its potential in many different fields (e.g., chemistry, physics, social networks). In this context, the Graph Neural Networking challenge 2021 brings a practical limitation of existing GNN-based solutions for networking: the lack of generalization to larger networks. This paper approaches the scalability problem by presenting a GNN-based solution that can effectively scale to larger networks including higher link capacities and aggregated traffic on links.

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Section: Related Workmentioning

confidence: 99%

Scaling Graph-based Deep Learning models to larger networks

Ferriol-Galmés,

Suárez-Varela,

Rusek

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…The approach considered in this paper is an example of supervised learning. Mestres et al [9] investigate modeling and prediction of delays in communication networks with feed-forward neural networks. They predict the latency based on the traf ic con iguration.…”

Section: Related Workmentioning

confidence: 99%

Graph-neural-network-based delay estimation for communication networks with heterogeneous scheduling policies

Happ¹,

Herlich²,

Maier³

et al. 2021

ITU-J FET

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Modeling communication networks to predict performance such as delay and jitter is important for evaluating and optimizing them. In recent years, neural networks have been used to do this, which may have advantages over existing models, for example from queueing theory. One of these neural networks is RouteNet, which is based on graph neural networks. However, it is based on simplified assumptions. One key simplification is the restriction to a single scheduling policy, which describes how packets of different flows are prioritized for transmission. In this paper we propose a solution that supports multiple scheduling policies (Strict Priority, Deficit Round Robin, Weighted Fair Queueing) and can handle mixed scheduling policies in a single communication network. Our solution is based on the RouteNet architecture as part of the "Graph Neural Network Challenge". We achieved a mean absolute percentage error under 1% with our extended model on the evaluation data set from the challenge. This takes neural-network-based delay estimation one step closer to practical use.

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“…Mestres et al [112] Sun et al [114] proposed an intelligent network control architectural framework that employs DRL to dynamically optimize routing plans in an SDN-enabled network without the need for human involvement. The proposed framework in called TIDE.…”

Section: B ML and Dl Techniques For Routing Optimization In Sdnmentioning

confidence: 99%

AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

et al. 2020

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The explosive growth of IP networks, the advent of cloud computing, and the rapid progress in wireless communications witnessed today reflect significant progress towards meeting the explosive data traffic demands. Consequently, communications service providers should deploy efficient and intelligent network solutions to accommodate the huge traffic demands and to ease the capacity pressure on their network infrastructure. Besides, vendors should develop novel energy-efficient networks to reduce network utility costs and carbon footprint. Software-defined networking (SDN) provides a suitable solution, however, complete SDN deployment is currently unachievable in the short-term. An alternative is the hybrid SDN/ open shortest path forwarding (OSPF) network, which allows the deployment of SDN in legacy networks. Nevertheless, hybrid SDN/OSPF also faces several technical, economic and organizational challenges. Although many energyefficiency routing solutions exist in hybrid SDN/OSPF networks, they are generic and reactive by design. Moreover, these solutions are characterized by manual control plane forwarding configurations, leading to sub-optimal performance. The recent promising combination of SDN and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) in traffic management and control provides tremendous opportunities. In this paper, we first provide a review of the most recent optimization approaches for global energy-efficient routing and load balancing. Next, we investigate a scalable and intelligent integrated architectural framework that leverages deep reinforcement learning (DRL) techniques to realize predictive and rate adaptive energy-efficient routing with guaranteed quality of service (QoS), in transitional hybrid SDN/OSPF networks. Based on the need to minimize global network energy consumption and improve link performance, this paper provides key research insights into the current progress in hybrid SDN/OSPF, ML and AI in the hope of stimulating more research.

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Understanding the Modeling of Computer Network Delays using Neural Networks

Cited by 55 publications

References 18 publications

Scaling Graph-based Deep Learning models to larger networks

Scaling Graph-based Deep Learning models to larger networks

Graph-neural-network-based delay estimation for communication networks with heterogeneous scheduling policies

AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

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