Help Privacy Policy Disclaimer
  Advanced SearchBrowse





Exploring Network-Wide Flow Data with Flowyager


Saidi,  Said Jawad
Internet Architecture, MPI for Informatics, Max Planck Society;


Maghsoudlou,  Aniss
Internet Architecture, MPI for Informatics, Max Planck Society;


Smaragdakis,  Georgios
Internet Architecture, MPI for Informatics, Max Planck Society;


Feldmann,  Anja       
Internet Architecture, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

(Preprint), 4MB

Supplementary Material (public)
There is no public supplementary material available

Saidi, S. J., Maghsoudlou, A., Foucard, D., Smaragdakis, G., Poese, I., & Feldmann, A. (2020). Exploring Network-Wide Flow Data with Flowyager. Retrieved from https://arxiv.org/abs/2010.13120.

Cite as: https://hdl.handle.net/21.11116/0000-0007-8562-4
Many network operations, ranging from attack investigation and mitigation to
traffic management, require answering network-wide flow queries in seconds.
Although flow records are collected at each router, using available traffic
capture utilities, querying the resulting datasets from hundreds of routers
across sites and over time, remains a significant challenge due to the sheer
traffic volume and distributed nature of flow records.
In this paper, we investigate how to improve the response time for a priori
unknown network-wide queries. We present Flowyager, a system that is built on
top of existing traffic capture utilities. Flowyager generates and analyzes
tree data structures, that we call Flowtrees, which are succinct summaries of
the raw flow data available by capture utilities. Flowtrees are self-adjusted
data structures that drastically reduce space and transfer requirements, by 75%
to 95%, compared to raw flow records. Flowyager manages the storage and
transfers of Flowtrees, supports Flowtree operators, and provides a structured
query language for answering flow queries across sites and time periods. By
deploying a Flowyager prototype at both a large Internet Exchange Point and a
Tier-1 Internet Service Provider, we showcase its capabilities for networks
with hundreds of router interfaces. Our results show that the query response
time can be reduced by an order of magnitude when compared with alternative
data analytics platforms. Thus, Flowyager enables interactive network-wide
queries and offers unprecedented drill-down capabilities to, e.g., identify
DDoS culprits, pinpoint the involved sites, and determine the length of the