2013Unpublished venueRequires access

A classification of network traffic status for various scale networks

Jaehwa Park, JunSeong Kim

Open publisher page 4 citations

Abstract

Techniques of network status estimation and traffic prediction are required for network control and user applications in the contexts where a variety of traffic data sources are available. Due to the difficulty of estimating applications' network demands and the difficulty of predicting network load, however, the management of network resources has often been ignored. This paper presents a heuristic of network status classification that has been observed in various scale operational networks. The basic idea of the approach is that network traffic repeat cycles of congested states and that a variation of network latency is strongly correlated with the past history of the latency. We directly monitor network load by continually measuring end-to-end network latencies in real operational networks and classify network traffic status with respect to the stability and the burstiness of the latencies. The experimental results showed that the proposed method is capable of evaluating network traffic status and reflecting the related fluctuations.

About this research paper

What this paper is about

Techniques of network status estimation and traffic prediction are required for network control and user applications in the contexts where a variety of traffic data sources are available. Due to the difficulty of estimating applications' network demands and the difficulty of predicting network load, however, the management of network resources has often been ignored. This paper presents a heuristic of network status classification that has been observed in various scale operational networks. The basic idea of the approach is that network traffic repeat cycles of congested states and that a variation of network latency is strongly correlated with the past history of the latency. We directly monitor network load by continually measuring end-to-end network latencies in real operational networks and classify network traffic status with respect to the stability and the burstiness of the latencies. The experimental results showed that the proposed method is capable of evaluating network traffic status and reflecting the related fluctuations.

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Techniques of network status estimation and traffic prediction are required for network control and user applications in the contexts where a variety of traffic data sources are available. Due to the difficulty of estimating applications' network demands and the difficulty of predicting network load, however, the management of network resources has often been ignored. This paper presents a heuristic of network status classification that has been observed in various scale operational networks. The basic idea of the approach is that network traffic repeat cycles of congested states and that a variation of network latency is strongly correlated with the past history of the latency. We directly monitor network load by continually measuring end-to-end network latencies in real operational networks and classify network traffic status with respect to the stability and the burstiness of the latencies. The experimental results showed that the proposed method is capable of evaluating network traffic status and reflecting the related fluctuations.

Key concepts: Burstiness, Network traffic control, Network traffic simulation, Computer science, Traffic generation model, Traffic shaping, Network management station, Network simulation

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