Delta-net is a tool that makes it possible to detect and prevent certain network outages before they occur. For this, Delta-net analyzes changes to the data plane in real-time and incrementally maintains flow information of every packet in the entire network. In our NSDI'17 paper, we propose the first provably quasi-linear algorithm to do so. Here, we provide the data sets that we used in our experiments.
If you are interested to learn more, please check out our publications. For any questions, feel also free to contact us.
Our NSDI'17 data set comprise several hundred million IP prefix rules generated using topologies and BGP updates from real-world deployed networks, including a globally deployed ONOS software-defined networking application, called SDN-IP. These data sets are organized as text files in which line denotes an insertion or removal of a rule. The format of such lines is explained in a short manual that describes Delta-net's interface.
Our experiments show that Delta-net checks a rule insertion and removal in tens of microseconds on average, a more than 10X improvement over the state-of-the-art. We also show that Delta-net eliminates an inherent bottleneck in the state-of-the-art that restricts its use in answering Datalog-style "what if" queries.
In our ICNP paper (see info below), we generalized Delta-net's lattice-theoretical analysis to handle multi-dimensional match conditions, e.g., ip-table rules.
@inproceedings{HKP2017,
author = {Horn, Alex and Kheradmand, Ali and Prasad, Mukul R.},
title = {Delta-net: Real-time Network Verification Using Atoms},
booktitle = {NSDI},
year = {2017},
}
@inproceedings{HKP2019,
author = {Horn, Alex and Kheradmand, Ali and Prasad, Mukul R.},
title = {A Precise and Expressive Lattice-theoretical Framework for Efficient Network Verification},
booktitle = {ICNP},
year = {2019},
}
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