Network Tomography and Performance Monitoring
Summary
Network tomography encompasses methods for inferring the internal characteristics of a communication network—such as topology, link delays, loss rates and jitter—using only end-to-end measurements between selected nodes. Performance monitoring complements these inferential techniques with active probes and passive data collection to assess quality of service (QoS), detect anomalies and guide traffic engineering. Together, these approaches address the challenges posed by large-scale and dynamic infrastructures—ranging from data centres and mobile backhaul to industrial and sensor networks—where direct observation of every link or router is impractical. Advances in statistical inference, algebraic coding, compressed sensing and machine learning have expanded the toolbox available to researchers and operators, enabling more accurate, scalable and robust performance estimates under partial topology knowledge, non-stationary traffic and dynamic routing.
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Network Tomography and Performance Monitoring publication trend
The graph below shows the total number of articles in network tomography and performance monitoring across all publications each year (not limited to Nature Index journals).
Technical terms
Network tomography: Inferential technique that estimates internal network properties (topology, delay, loss) from measurements taken at network edges.
End-to-end measurement: Collection of performance data (e.g. round-trip delay, packet loss) between source and destination nodes without accessing intermediate devices.
Dynamic routing: Routing paradigm in which paths between nodes can change in response to network state or policies, introducing nondeterminism into inference tasks.
Graph convolutional network (GCN): Neural network architecture designed to operate on graph-structured data by aggregating and transforming features across node neighbourhoods.
References
- Network Tomography with Partial Topology Knowledge and Dynamic Routing. Journal of Network and Systems Management (2023).
- A Graph Convolutional Network-Based Method for Congested Link Identification. Applied Sciences (2024).
- Topology Inference and Link Parameter Estimation Based on End-to-End Measurements †. Future Internet (2022).
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