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Brief announcement: parameterized maximum and average degree approximation in topic-based publish-subscribe overlay network design
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ACM Symposium on Parallel Algorithms and Architectures archive
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures table of contents
Calgary, AB, Canada
SESSION: Brief Announcements I table of contents
Pages 39-40  
Year of Publication: 2009
ISBN:978-1-60558-606-9
Authors
Melih Onus  Arizona State University, Tempe, AZ, USA
Andréa W. Richa  Arizona State University, Tempe, AZ, USA
Sponsors
SIGOPS: ACM Special Interest Group on Operating Systems
ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
Publisher
ACM  New York, NY, USA
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ABSTRACT

Designing an overlay network for publish/subscribe communication in a system where nodes may subscribe to many different topics of interest is of fundamental importance. For scalability and efficiency, it is important to keep the degree of the nodes in the publish/subscribe system low. It is only natural then to formalize the following problem: Given a collection of nodes and their topic subscriptions connect the nodes into a graph which has low average and maximum degree and in such a way that for each topic t, the graph induced by the nodes interested in t is connected. We present the first polynomial time parameterized sublinear approximation algorithm for this problem.


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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M. Onus and A.W. Richa. Parameterized Maximum and Average Degree Approximation in Topic-based Publish-Subscribe Overlay Network Design. In Technical Report, Arizona State University, Department of Computer Science and Engineering, TR-09-006, 2009.
 
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M. Onus and A.W. Richa. Minimum maximum Degree Publish-Subscribe Overlay Network Design. In INFOCOM, 2009.

Collaborative Colleagues:
Melih Onus: colleagues
Andréa W. Richa: colleagues