Name
Abstract | ||
|---|---|---|
Advances in networking technology have enabled network engineers to use sampled data from routers to estimate network flow volumes and track them over time. However, low sampling rates result in large noise in traffic volume estimates. We propose to combine data on individual flows obtained from sampling with highly aggregate data obtained from SNMP measurements (similar to those used in network tomography) for the tracking problem at hand. Specifically, we introduce a linearized state space model for the estimation of network traffic flow volumes from combined SNMP and sampled data. Further, we formulate the problem of obtaining optimal sampling rates under router resource constraints as an experiment design problem. Theoretically it corresponds to the problem of optimal design for estimation of conditional means for state space models and we present the associated convex programs for a simple approach to it. The usefulness of the approach in the context of network monitoring is illustrated through an extensive numerical study. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1375457.1375474 | SIGMETRICS'08: Proceedings of the 2008 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems |
Keywords | Field | DocType |
measurement,design,internet traffic,experience design,network monitoring,network tomography,convex programming,network flow,state space model,optimal design,kalman filter,kalman filtering | Flow network,Traffic generation model,Mathematical optimization,Network planning and design,Computer science,Network simulation,Real-time computing,Network tomography,Sampling (statistics),Network monitoring,State space | Conference |
Volume | Issue | ISSN |
36 | 1 | 0163-5999 |
Citations | PageRank | References |
10 | 0.81 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harsh Singhal | 1 | 17 | 1.76 |
| George Michailidis | 2 | 303 | 35.19 |