Incorporating Intra-flow Dependencies and Inter-flow Correlations for Traffic Matrix Prediction

Gao, Kaihui; Li, Dan; Chen, Li; Geng, Jinkun; Gui, Fei; Yang, Cheng; Gu, Yue

doi:10.1109/iwqos49365.2020.9213008

Cited by 18 publications

(1 citation statement)

References 26 publications

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“…Past works showed that network traffic traces are far from being purely random, meaning that they intrinsically have some underlying structure [1], [12]- [15]. In particular, traffic traces are known to present spatial and temporal patterns that could potentially be exploited to increase current compression ratios.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Spatial and Temporal Correlations for Network Traffic Compression

Almasan¹,

Rusek²,

Xiao³

et al. 2023

Preprint

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The deployment of modern network applications is increasing the network size and traffic volumes at an unprecedented pace. Storing network-related information (e.g., traffic traces) is key to enable efficient network management. However, this task is becoming more challenging due to the ever-increasing data transmission rates and traffic volumes. In this paper, we present a novel method for network traffic compression that exploits spatial and temporal patterns naturally present in network traffic. We consider a realistic scenario where traffic measurements are performed at multiple links of a network topology using tools like SNMP or NetFlow. Such measurements can be seen as multiple time series that exhibit spatial and temporal correlations induced by the network topology, routing or user behavior. Our method leverages graph learning methods to effectively exploit both types of correlations for traffic compression. The experimental results show that our solution is able to outperform GZIP, the de facto traffic compression method, improving by 50%-65% the compression ratio on three realworld networks.

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Section: Introductionmentioning

confidence: 99%