Effect of bursty traffic on the performance of heterogeneous access networks

Mohamed, Ahmed M.; Agamy, Adel

doi:10.1109/itce.2018.8316598

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2024

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“…Recurrent neural networks applied to dynamic states of computer networks, in the form of time series, are employed for congestion control forecasting and anomaly detection [13]. These methods are overall flexible in design, but transferring traffic knowledge between different areas of complex networks while preserving highly-accurate predictions is challenging [14], [15].…”

Section: A Related Workmentioning

confidence: 99%

Traffic Divergence Theory: An Analysis Formalism for Dynamic Networks

Macktoobian,

Shu,

Zhao

2024

IEEE Access

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Traffic dynamics is universally crucial in analyzing and designing almost any network. This article introduces a novel theoretical approach to analyzing network traffic dynamics. This theory's machinery is based on the notion of traffic divergence, which captures the flow (im)balance of network nodes and links. It features various analytical probes to investigate both spatial and temporal traffic dynamics. In particular, the maximal traffic distribution in a network can be characterized by spatial traffic divergence rate, which reveals the relative difference among node traffic divergence. To illustrate the usefulness, we apply the theory to two network-driven problems: throughput estimation of data center networks and power-optimized communication planning for robot networks, and show the merits of the proposed theory through simulations.

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Section: A Related Workmentioning

confidence: 99%