Multi‐scale Internet traffic forecasting using neural networks and time series methods

Cortez, Paulo; Rio, Miguel; Rocha, Miguel; Sousa, Pedro

doi:10.1111/j.1468-0394.2010.00568.x

Cited by 160 publications

(112 citation statements)

References 19 publications

(53 reference statements)

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“…Other authors attempted to model TCP throughput as time-series and to use other tools for prediction, such as Support Vector Regression [17], neural networks [21,22], autoregresive and linear regression models [23,24]. To the best of our knowledge, there are no other similar works modeling TCP throughput as time-series and using GA for forecasting.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Many researchers have been focusing on studying and forecasting bandwidth demands so as to properly use and distribute the available resources. As many applications have been shifting towards TCP/IP based networks [1], the need for further research on TCP/IP throughput prediction is evident. Also, nowadays multi-homing ca-pabilities enable concurrent data transmissions over different interfaces.…”

Section: Introductionmentioning

confidence: 99%

“…However, a prediction over time requires frequent packet pairs to be sent which may translate into high overhead and low prediction quality. Finally, history based techniques try to model the TCP throughput evolution over time as a time series and apply tools such as neural networks to find patterns [1]. Based on such patterns, one tries to predict future TCP throughput over time.…”

Section: Introductionmentioning

confidence: 99%

“…To this end and to keep the study easy to understand, we focus on two representative TCP traces obtained in a Wireless LAN environment. Several fitness functions and selection methods of the GA are analyzed and combined in order to fully understand their impact on the prediction in two scenarios: one with a regular pattern and another one with abrupt changes in the trend 1 .…”

Section: Introductionmentioning

confidence: 99%

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Predicting expected TCP throughput using genetic algorithm

2016

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Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting expected TCP throughput using genetic algorithm

2016

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“…Network performance, as captured by throughput, packet delay and loss rate, degrades gradually with increased long range dependence (LRD) or self-similarity of aggregated traffic [17]. Simultaneously, network routers and other middle-boxes servicing such traffic experience larger queueing delay and response time [18]. Therefore, from the traffic engineering point of view, an estimation of the degree of LRD for the examined video traffic may be useful, e.g.…”

Section: Multiplexed Traffic and Its Characteristicmentioning

confidence: 99%

Analysis and modelling of traffic produced by adaptive HTTP-based video

Biernacki

2016

Multimed Tools Appl

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The increase of HTTP-based video popularity causes that broadband and Internet service providers' links transmit mainly multimedia content. Network planning, traffic engineering or congestion control requires an understanding of the statistical properties of network traffic; therefore, it is desirable to investigate the characteristic of traffic traces generated by systems which employ adaptive bit-rate streaming. Our first contribution is an investigation of traffic originating from 120 client-server pairs, situated in an emulated content distribution network, and multiplexed onto a single network link. We show that the structure of the traffic is distinct from the structure generated by the first and second generation of HTTP video systems, and furthermore, not similar to the structure of general Internet traffic. The obtained traffic exhibits negative and positive correlations, anti-persistence, and its distribution function is skewed to the right. Our second contribution is an approximation of the traffic by ARIMA/FARIMA processes blue and artificial neural networks. As we show, the obtained traffic models are able to enhance the performance of an adaptive streaming algorithm.

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Network traffic prediction method based on echo state network with adaptive reservoir

et al. 2020

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Network traffic prediction is of great significance to resource management in cyber‐physical systems (CPSs). In particular, network traffic is a nonlinear time series. Echo state network (ESN) is a new neural network with strong nonlinear processing capacity and short‐term memory capacity, and thus can achieve good performance in predicting nonlinear time series. However, network traffic has various characteristics such as self‐similarity, chaos, mutability. As the core of ESN, the reservoir will be fixed rather than adjustable once it is generated, which limits the prediction performance of ESN in different network traffic. To achieve universal excellent prediction performance, this paper proposes a new network traffic prediction method based on ESN with adaptive reservoir (ESN‐AR). First, the framework of ESN‐AR is constructed for network traffic prediction, in which the idea of generative adversarial network (GAN) is incorporated into ESN to adaptively adjust the reservoir. Specifically, ESN is used as the generative model to predict network traffic and feedforward neural network (FNN) is used as the discriminative model to distinguish between the real network traffic and the predicted network traffic. Second, the adversarial training algorithm of ESN‐AR is proposed to obtain the appropriate reservoir depending on the network traffic characteristics. Finally, ESN‐AR is applied to the prediction of three actual network traffic with different characteristics. Simulation results show that compared with the state‐of‐the‐art models, the proposed method achieves more accurate and stable prediction performance.

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Multi‐scale Internet traffic forecasting using neural networks and time series methods

Cited by 160 publications

References 19 publications

Predicting expected TCP throughput using genetic algorithm

Predicting expected TCP throughput using genetic algorithm

Analysis and modelling of traffic produced by adaptive HTTP-based video

Network traffic prediction method based on echo state network with adaptive reservoir

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