Subspace Properties of Network Coding and their Applications

TL;DR

This paper explores the properties of subspaces generated by randomized network coding, revealing their potential to infer network topology, identify bottlenecks, and detect malicious nodes, thereby enhancing network management.

Contribution

It establishes fundamental properties of random subspaces in network coding and demonstrates their applications in topology inference, bottleneck detection, and security.

Findings

Subspaces carry topological information about the network.

Randomized network coding aids in passive network monitoring.

Applications include topology inference and Byzantine attack detection.

Abstract

Systems that employ network coding for content distribution convey to the receivers linear combinations of the source packets. If we assume randomized network coding, during this process the network nodes collect random subspaces of the space spanned by the source packets. We establish several fundamental properties of the random subspaces induced in such a system, and show that these subspaces implicitly carry topological information about the network and its state that can be passively collected and inferred. We leverage this information towards a number of applications that are interesting in their own right, such as topology inference, bottleneck discovery in peer-to-peer systems and locating Byzantine attackers. We thus argue that, randomized network coding, apart from its better known properties for improving information delivery rate, can additionally facilitate network management and control.