Title:
Scalable and Efficient Data Streaming Algorithms for Detecting Common Content in Internet Traffic
Scalable and Efficient Data Streaming Algorithms for Detecting Common Content in Internet Traffic
Author(s)
Sung, Min-Ho
Kumar, Abhishek
Li, Li (Erran)
Wang, Jia
Xu, Jun
Kumar, Abhishek
Li, Li (Erran)
Wang, Jia
Xu, Jun
Advisor(s)
Editor(s)
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Abstract
Recent research on data streaming algorithms has
provided powerful tools to efficiently monitor various characteristics
of traffic passing through a single network link or node. However,
it is often desirable to perform data streaming analysis on the
traffic aggregated over hundreds or even thousands of links/nodes,
which will provide network operators with a holistic view of the
network operation. Shipping raw traffic data to a centralized location
(i.e., "raw aggregation") for streaming analysis is clearly
not a feasible approach for a large network. In this paper, we
propose a set of novel Distributed Collaborative Streaming (DCS)
algorithms that allow scalable and efficient monitoring of aggregated
traffic without the need for raw aggregation. Our algorithms
target the specific network monitoring problem of finding common
content in the Internet traffic traversing several nodes/links, which
has applications in network-wide intrusion detection, early warning
for fast propagating worms, and detection of hot objects and
spam traffic. We evaluate our algorithms through extensive simulations
and experiments on traffic traces collected from a tier-1
ISP. The experimental results demonstrate that our algorithms can
effectively detect common content in the traffic traversing across a
large network.
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Date Issued
2006
Extent
332931 bytes
Resource Type
Text
Resource Subtype
Technical Report