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Network-Wide Heavy-Hitter Detection for Real-Time Telemetry
| Content Provider | Semantic Scholar |
|---|---|
| Author | Cai, Qizhe |
| Copyright Year | 2018 |
| Abstract | Many network monitoring tasks identify subsets of traffic that stand out, e.g., topk flows for a particular statistic. We can efficiently determine these “heavy-hitter” flows on individual network elements, but network operators often want to identify interesting traffic on a network-wide basis. Determining the heavy hitters on a network-wide basis necessarily introduces a trade-off between the communication required to perform this distributed computation and the accuracy of the results. To perform distributed heavy-hitter detection in real time with high accuracy and low overhead, we extend the Continuous Distributed Monitoring (CDM) model to account for the realities of modern networks and devise practical solutions that detect heavy hitters with high accuracy and low communication overhead. We present two novel algorithms that automatically tune the set of monitoring switches in response to traffic dynamics. We implement our system using the P4 language, and evaluate it using real-world packet traces. We demonstrate that our solutions can accurately detect network-wide heavy hitters with up to 70% savings in communication overhead compared to existing approaches. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.cs.princeton.edu/~jrex/thesis/qizhe-cai-thesis.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
