Monitoring the Dynamics of Network Traffic by Recursive Multi-Dimensional Aggregation

Midori Kato, Kenjiro Cho, Michio Honda, Hideyuki Tokuda

Research output: Contribution to conferencePaperpeer-review

Abstract

A promising way to capture the characteristics of changing traffic is to extract significant flow clusters in traffic. However, clustering flows by 5-tuple requires flow matching in huge flow attribute spaces, and thus, is difficult to perform on the fly. We propose an efficient yet flexible flow aggregation technique for monitoring the dynamics of network traffic. Our scheme employs two-stage flow-aggregation. The primary aggregation stage is for efficiently processing a huge volume of raw traffic records. It first aggregates each attribute of 5-tuple separately, and then, produces multi-dimensional flows by matching each attribute of a flow to the resulted aggregated attributes. The secondary aggregation stage is for providing flexible views to operators. It performs multi-dimensional aggregation with the R-tree algorithm to produce concise summaries for operators. We report our prototype implementation and preliminary results using traffic traces from backbone networks.
Original languageEnglish
Number of pages7
Publication statusPublished - 12 Oct 2012
Event2012 Workshop on Managing Systems Automaticall and Dynamically - Hollywood, United States
Duration: 7 Oct 20127 Oct 2012
https://www.usenix.org/conference/mad12

Workshop

Workshop2012 Workshop on Managing Systems Automaticall and Dynamically
Abbreviated titleMAD '12
CountryUnited States
CityHollywood
Period7/10/127/10/12
Internet address

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