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MOSAIC: unified declarative platform for dynamic overlay composition
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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. 5  
Year of Publication: 2008
ISBN:978-1-60558-210-8
Authors
Yun Mao  University of Pennsylvania and AT&T Labs - Research
Boon Thau Loo  University of Pennsylvania
Zachary Ives  University of Pennsylvania
Jonathan M. Smith  University of Pennsylvania
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

Overlay networks create new networking services across nodes that communicate using pre-existing networks. MOSAIC is a unified declarative platform for constructing new overlay networks from multiple existing overlays, each possessing a subset of the desired new network's characteristics. MOSAIC overlays are specified using Mozlog, a new declarative language for expressing overlay properties independently from their particular implementation or underlying network.

This paper focuses on the runtime aspects of MOSAIC: composition and deployment of control and/or data plane functions of different overlay networks, dynamic compositions of overlay networks to meet changing application needs and network conditions, and seamless support for legacy applications. MOSAIC is validated experimentally using compositions specified in Mozlog: we combine an indirection overlay that supports mobility (i3), a resilient overlay (RON), and scalable lookups (Chord), to provide new overlay networks with new functions. MOSAIC uses runtime composition to simultaneously deliver application-aware mobility, NAT traversal and reliability. We further demonstrate MOSAIC'S dynamic composition capabilities by Chord switching its underlay from IP to RON at runtime. These benefits are obtained at a low performance cost, as demonstrated by measurements on both a local cluster and PlanetLab.


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:
Yun Mao: colleagues
Boon Thau Loo: colleagues
Zachary Ives: colleagues
Jonathan M. Smith: colleagues