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RFlow+: An SDN-based WLAN monitoring and management framework
- Source :
- INFOCOM
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- In this work, we propose an SDN-based WLAN monitoring and management framework called RFlow+ to address WiFi service dissatisfaction caused by the limited view (lack of scalability) of network traffic monitoring and absence of intelligent and timely network treatments. Existing solutions (e.g., OpenFlow and sFlow) have limited view, no generic flow description, and poor trade-off between measurement accuracy and network overhead depending on the selection of the sampling rate. To resolve these issues, we devise a two-level counting mechanism, namely a distributed local counter (on-site and real-time) and central collector (a summation of local counters). With this, we proposed a highly scalable monitoring and management framework to handle immediate actions based on short-term (e.g., 50 ms) monitoring and eventual actions based on long-term (e.g., 1 month) monitoring. The former uses the local view of each access point (AP), and the latter uses the global view of the collector. Experimental results verify that RFlow+ can achieve high accuracy (less than 5% standard error for short-term and less than 1% for long-term) and fast detection of flows of interest (within 23 ms) with manageable network overhead. We prove the practicality of RFlow+ by showing the effectiveness of a MAC flooding attacker quarantine in a real-world testbed.
- Subjects :
- OpenFlow
Service (systems architecture)
sFlow
Computer science
business.industry
Testbed
020206 networking & telecommunications
02 engineering and technology
MAC flooding
Scalability
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Point (geometry)
business
Computer network
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- IEEE INFOCOM 2017 - IEEE Conference on Computer Communications
- Accession number :
- edsair.doi...........f6a560932f6fb3a51cfd6d1448d99676
- Full Text :
- https://doi.org/10.1109/infocom.2017.8056995