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DaVinci: dynamically adaptive virtual networks for a customized internet
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Source International Conference On Emerging Networking Experiments And Technologies archive
Proceedings of the 2008 ACM CoNEXT Conference table of contents
Madrid, Spain
Article No. 15  
Year of Publication: 2008
ISBN:978-1-60558-210-8
Authors
Jiayue He  Princeton University
Rui Zhang-Shen  Princeton University
Ying Li  Princeton University
Cheng-Yen Lee  Princeton University
Jennifer Rexford  Princeton University
Mung Chiang  Princeton University
Sponsors
ACM: Association for Computing Machinery
SIGCOMM: ACM Special Interest Group on Data Communication
Publisher
ACM  New York, NY, USA
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ABSTRACT

Running multiple virtual networks, customized for different performance objectives, is a promising way to support diverse applications over a shared substrate. Despite being simple, a static division of resources between virtual networks can be highly inefficient, while dynamic resource allocation runs the risk of instability. This paper uses optimization theory to show that adaptive resource allocation can be stable and can maximize the aggregate performance across the virtual networks. In the DaVinci architecture, each substrate link periodically reassigns bandwidth shares between its virtual links; while at a smaller timescale, each virtual network runs a distributed protocol that maximizes its own performance objective independently. Numerical experiments with a mix of delay-sensitive and throughput-sensitive traffic show that the bandwidth shares converge quickly to the optimal values. We demonstrate that running several custom protocols in parallel and allocating resource adaptively can be more efficient, more flexible, and easier to manage than a compromise "one-size-fits-all" design.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Jiayue He: colleagues
Rui Zhang-Shen: colleagues
Ying Li: colleagues
Cheng-Yen Lee: colleagues
Jennifer Rexford: colleagues
Mung Chiang: colleagues