DeepFlow: A Deep Learning Framework For Software Defined Measurement
In Proc. of the Cloud-Assisted Networking Workshop (in conjunction with ACM CoNEXT 2017), Seoul Korea
Overview
Introduced DeepFlow: SDN measurement that combines exact flow counters on hardware switches with cloud-trained deep learning predictions for flows that don't fit in switch memory.
Key contributions
- Adaptive detection of the most active IP prefixes, measured at fine granularity, with coarser counters elsewhere.
- LSTM models that predict flow sizes when exact counters cannot be installed.
- Measures far more flows than sampling-based approaches, without their accuracy loss.
Abstract
Providing fine grained traffic measurement is crucial for many network management tasks such as traffic engineering, anomaly detection, traffic accounting, and load balancing. Software-defined networks can potentially enable fine-grained measurement by providing statistics for each forwarding rule of an OpenFlow-enabled switch. However, providing fine grained traffic measurement in a scalable fashion in hardware switches poses significant challenges due to the limitations in the size of the TCAMs that fit only a relatively small number of rules compared to the number of active flows in the network. In this paper, we present DeepFlow, a framework for scalable software-defined measurement that relies on an efficient mechanism that a) adaptively detect the most active source and destination prefixes in the network, b) collects fine-grained flow-size measurements for the most active prefixes and coarse grained for the less active ones, and c) uses historical measurements in order to train a cloud-based Deep Learning model that can be used to provide short-term predictions whenever exact flow counters cannot be placed at a switch due to its limited resources. Thus the number of fine-grained flows measured can increase significantly without the need to use other flow sampling solutions that loose accuracy. An extensive experimental evaluation using a prototype implementation and real network traces shows that DeepFlow can provide very high accuracy for estimating flow sizes at various aggregation levels.
Cite this paper
@inproceedings{DBLP:conf/conext/LazarisP17,
author = {Aggelos Lazaris and
Viktor K. Prasanna},
title = {DeepFlow: a deep learning framework for software-defined measurement},
booktitle = {Proceedings of the 2nd Workshop on Cloud-Assisted Networking, CAN@CoNEXT
2017, Incheon, Republic of Korea, December 12, 2017},
pages = {43--48},
year = {2017},
crossref = {DBLP:conf/conext/2017can},
url = {https://doi.org/10.1145/3155921.3155922},
doi = {10.1145/3155921.3155922},
timestamp = {Tue, 06 Nov 2018 16:58:31 +0100},
biburl = {https://dblp.org/rec/bib/conf/conext/LazarisP17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}