4,284 research outputs found
Topology aware Internet traffic forecasting using neural networks
Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detection tools can be developed, leading to economic gains due to better resource management. This paper presents a Neural Network (NN) approach to predict TCP/IP traffic for all links of a backbone network, using both univariate and multivariate strategies. The former uses only past values of the forecasted link, while the latter is based on the neighbor links of the backbone topology. Several experiments were held by considering real-world data from the UK education and research network. Also, different time scales (e.g. every ten minutes and hourly) were analyzed. Overall, the proposed NN approach outperformed other forecasting methods (e.g. Holt-Winters).R&D Algoritmi centr
Knowledge-defined networking : a machine learning based approach for network and traffic modeling
The research community has considered in the past the application of Machine Learning (ML) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this thesis, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of ML techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and ML, and provide use-cases that illustrate its applicability and benefits. We also present some relevant use-cases, in which ML tools can be useful. We refer to this new paradigm as Knowledge-Defined Networking (KDN).
In this context, ML can be used as a network modeling technique to build models that estimate the network performance. Network modeling is a central technique to many networking functions, for instance in the field of optimization. One of the objective of this thesis is to provide an answer to the following question: Can neural networks accurately model the performance of a computer network as a function of the input traffic?. In this thesis, we focus mainly on modeling the average delay, but also on estimating the jitter and the packets lost. For this, we assume the network as a black-box that has as input a traffic matrix and as output the desired performance matrix. Then we train different regressors, including deep neural networks, and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. Moreover, we also study the impact of having multiple traffic flows between each pair of nodes.
We also explore the use of ML techniques in other network related fields. One relevant application is traffic forecasting. Accurate forecasting enables scaling up or down the resources to efficiently accommodate the load of traffic. Such models are typically based on traditional time series ARMA or ARIMA models. We propose a new methodology that results from the combination of an ARIMA model with an ANN. The Neural Network greatly improves the ARIMA estimation by modeling complex and nonlinear dependencies, particularly for outliers. In order to train the Neural Network and to improve the outliers estimation, we use external information: weather, events, holidays, etc. The main hypothesis is that network traffic depends on the behavior of the end-users, which in turn depend on external factors. We evaluate the accuracy of our methodology using real-world data from an egress Internet link of a campus network. The analysis shows that the model works remarkably well, outperforming standard ARIMA models.
Another relevant application is in the Network Function Virtualization (NFV). The NFV paradigm makes networks more flexible by using Virtual Network Functions (VNF) instead of dedicated hardware. The main advantage is the flexibility offered by these virtual elements. However, the use of virtual nodes increases the difficulty of modeling such networks. This problem may be addressed by the use of ML techniques, to model or to control such networks. As a first step, we focus on the modeling of the performance of single VNFs as a function of the input traffic. In this thesis, we demonstrate that the CPU consumption of a VNF can be estimated only as a function of the input traffic characteristics.L'aplicació de tècniques d'aprenentatge automàtic (ML) pel control i operació de xarxes informàtiques ja s'ha plantejat anteriorment per la comunitat científica. Un exemple important és "Knowledge Plane", proposat per D. Clark et al. Tot i això, aquestes propostes no s'han utilitzat ni implementat mai en aquest camp. En aquesta tesi, explorem els motius que han fet impossible l'adopció fins al present, i que ara en permeten la implementació. El principal motiu és l'adopció de dos nous paradigmes: Software-Defined Networking (SDN) i Network Analytics (NA), que permeten la utilització de tècniques d'aprenentatge automàtic en el context de control i operació de xarxes informàtiques. En aquesta tesi, es descriu aquest paradigma, que aprofita les possibilitats ofertes per SDN, per NA i per ML, i s'expliquen aplicacions en el món de la informàtica i les comunicacions on l'aplicació d'aquestes tècniques poden ser molt beneficioses. Hem anomenat a aquest paradigma Knowledge-Defined Networking (KDN). En aquest context, una de les aplicacions de ML és el modelatge de xarxes informàtiques per estimar-ne el comportament. El modelatge de xarxes és un camp de recerca important el aquest camp, i que permet, per exemple, optimitzar-ne el seu rendiment. Un dels objectius de la tesi és respondre la següent pregunta: Pot una xarxa neuronal modelar de manera acurada el comportament d'una xarxa informàtica en funció del tràfic d'entrada? Aquesta tesi es centra principalment en el modelatge del retard mig (temps entre que s'envia i es rep un paquet). També s'estudia com varia aquest retard (jitter) i el nombre de paquets perduts. Per fer-ho, s'assumeix que la xarxa és totalment desconeguda i que només es coneix la matriu de tràfic d'entrada i la matriu de rendiment com a sortida. Es fan servir diferents tècniques de ML, com ara regressors lineals i xarxes neuronals, i se n'avalua la precisió per diferents xarxes i diferents configuracions de xarxa i tràfic. Finalment, també s'estudia l'impacte de tenir múltiples fluxos entre els parells de nodes. En la tesi, també s'explora l'ús de tècniques d¿aprenentatge automàtic en altres camps relacionats amb les xarxes informàtiques. Un cas rellevant és la predicció de tràfic. Una bona estimació del tràfic permet preveure la utilització dels diversos elements de la xarxa i optimitzar-ne el seu rendiment. Les tècniques tradicionals de predicció de tràfic es basen en tècniques de sèries temporals, com ara models ARMA o ARIMA. En aquesta tesis es proposa una nova metodologia que combina un model ARIMA amb una xarxa neuronal. La xarxa neuronal millora la predicció dels valors atípics, que tenen comportament complexos i no lineals. Per fer-ho, s'incorpora a l'anàlisi l'ús d'informació externa, com ara: informació meteorològica, esdeveniments, vacances, etc. La hipòtesi principal és que el tràfic de xarxes informàtiques depèn del comportament dels usuaris finals, que a la vegada depèn de factors externs. Per això, s'avalua la precisió de la metodologia presentada fent servir dades reals d'un enllaç de sortida de la xarxa d'un campus. S'observa que el model presentat funciona bé, superant la precisió de models ARIMA estàndards. Una altra aplicació important és en el camp de Network Function Virtualization (NFV). El paradigma de NFV fa les xarxes més flexibles gràcies a l'ús de Virtual Network Functions (VNF) en lloc de dispositius específics. L'avantatge principal és la flexibilitat que ofereixen aquests elements virtuals. Per contra, l'ús de nodes virtuals augmenta la dificultat de modelar aquestes xarxes. Aquest problema es pot estudiar també mitjançant tècniques d'aprenentatge automàtic, tant per modelar com per controlar la xarxa. Com a primer pas, aquesta tesi es centra en el modelatge del comportament de VNFs treballant soles en funció del tràfic que processen. Concretament, es demostra que el consum de CPU d'una VNF es pot estimar a partir a partir de diverses característiques del tràfic d'entrada.Postprint (published version
Quality-Aware Broadcasting Strategies for Position Estimation in VANETs
The dissemination of vehicle position data all over the network is a
fundamental task in Vehicular Ad Hoc Network (VANET) operations, as
applications often need to know the position of other vehicles over a large
area. In such cases, inter-vehicular communications should be exploited to
satisfy application requirements, although congestion control mechanisms are
required to minimize the packet collision probability. In this work, we face
the issue of achieving accurate vehicle position estimation and prediction in a
VANET scenario. State of the art solutions to the problem try to broadcast the
positioning information periodically, so that vehicles can ensure that the
information their neighbors have about them is never older than the
inter-transmission period. However, the rate of decay of the information is not
deterministic in complex urban scenarios: the movements and maneuvers of
vehicles can often be erratic and unpredictable, making old positioning
information inaccurate or downright misleading. To address this problem, we
propose to use the Quality of Information (QoI) as the decision factor for
broadcasting. We implement a threshold-based strategy to distribute position
information whenever the positioning error passes a reference value, thereby
shifting the objective of the network to limiting the actual positioning error
and guaranteeing quality across the VANET. The threshold-based strategy can
reduce the network load by avoiding the transmission of redundant messages, as
well as improving the overall positioning accuracy by more than 20% in
realistic urban scenarios.Comment: 8 pages, 7 figures, 2 tables, accepted for presentation at European
Wireless 201
The Challenges in SDN/ML Based Network Security : A Survey
Machine Learning is gaining popularity in the network security domain as many
more network-enabled devices get connected, as malicious activities become
stealthier, and as new technologies like Software Defined Networking (SDN)
emerge. Sitting at the application layer and communicating with the control
layer, machine learning based SDN security models exercise a huge influence on
the routing/switching of the entire SDN. Compromising the models is
consequently a very desirable goal. Previous surveys have been done on either
adversarial machine learning or the general vulnerabilities of SDNs but not
both. Through examination of the latest ML-based SDN security applications and
a good look at ML/SDN specific vulnerabilities accompanied by common attack
methods on ML, this paper serves as a unique survey, making a case for more
secure development processes of ML-based SDN security applications.Comment: 8 pages. arXiv admin note: substantial text overlap with
arXiv:1705.0056
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Study of the Application of Neural Networks in Internet Traffic Engineering
In this study, we showed various approachs implemented in Artificial Neural Networks for network
resources management and Internet congestion control. Through a training process, Neural Networks can
determine nonlinear relationships in a data set by associating the corresponding outputs to input patterns.
Therefore, the application of these networks to Traffic Engineering can help achieve its general objective:
“intelligent” agents or systems capable of adapting dataflow according to available resources. In this article, we
analyze the opportunity and feasibility to apply Artificial Neural Networks to a number of tasks related to Traffic
Engineering. In previous sections, we present the basics of each one of these disciplines, which are associated to
Artificial Intelligence and Computer Networks respectively
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services
Graph Neural Networks (GNNs) have gained growing interest in miscellaneous
applications owing to their outstanding ability in extracting latent
representation on graph structures. To render GNN-based service for IoT-driven
smart applications, traditional model serving paradigms usually resort to the
cloud by fully uploading geo-distributed input data to remote datacenters.
However, our empirical measurements reveal the significant communication
overhead of such cloud-based serving and highlight the profound potential in
applying the emerging fog computing. To maximize the architectural benefits
brought by fog computing, in this paper, we present Fograph, a novel
distributed real-time GNN inference framework that leverages diverse and
dynamic resources of multiple fog nodes in proximity to IoT data sources. By
introducing heterogeneity-aware execution planning and GNN-specific compression
techniques, Fograph tailors its design to well accommodate the unique
characteristics of GNN serving in fog environments. Prototype-based evaluation
and case study demonstrate that Fograph significantly outperforms the
state-of-the-art cloud serving and fog deployment by up to 5.39x execution
speedup and 6.84x throughput improvement.Comment: Accepted by IEEE/ACM Transactions on Networkin
Using artificial intelligence to support emerging networks management approaches
In emergent networks such as Internet of Things (IoT) and 5G applications, network traffic estimation is of great importance to forecast impacts on resource allocation that can influence the quality of service. Besides, controlling the network delay caused with route selection is still a notable challenge, owing to the high mobility of the devices. To analyse the trade-off between traffic forecasting accuracy and the complexity of artificial intelligence models used in this scenario, this work first evaluates the behavior of several traffic load forecasting models in a resource sharing environment. Moreover, in order to alleviate the routing problem in highly dynamic ad-hoc networks, this work also proposes a machine-learning-based routing scheme to reduce network delay in the high-mobility scenarios of flying ad-hoc networks, entitled Q-FANET. The performance of this new algorithm is compared with other methods using the WSNet simulator. With the obtained complexity analysis and the performed simulations, on one hand the best traffic load forecast model can be chosen, and on the other, the proposed routing solution presents lower delay, higher packet delivery ratio and lower jitter in highly dynamic networks than existing state-of-art methods
Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting
Traffic forecasting is essential for the traffic construction of smart cities
in the new era. However, traffic data's complex spatial and temporal
dependencies make traffic forecasting extremely challenging. Most existing
traffic forecasting methods rely on the predefined adjacency matrix to model
the Spatio-temporal dependencies. Nevertheless, the road traffic state is
highly real-time, so the adjacency matrix should change dynamically with time.
This article presents a new Multi-Spatio-temporal Fusion Graph Recurrent
Network (MSTFGRN) to address the issues above. The network proposes a
data-driven weighted adjacency matrix generation method to compensate for
real-time spatial dependencies not reflected by the predefined adjacency
matrix. It also efficiently learns hidden Spatio-temporal dependencies by
performing a new two-way Spatio-temporal fusion operation on parallel
Spatio-temporal relations at different moments. Finally, global Spatio-temporal
dependencies are captured simultaneously by integrating a global attention
mechanism into the Spatio-temporal fusion module. Extensive trials on four
large-scale, real-world traffic datasets demonstrate that our method achieves
state-of-the-art performance compared to alternative baselines
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