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Greedy distributed optimization of multi-commodity flows
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Annual ACM Symposium on Principles of Distributed Computing archive
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing table of contents
Portland, Oregon, USA
SESSION: Economical aspects table of contents
Pages: 274 - 283  
Year of Publication: 2007
ISBN:978-1-59593-616-5
Authors
Baruch Awerbuch  Johns Hopkins University
Rohit Khandekar  IBM T.J. Watson Research Center
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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Downloads (6 Weeks): 4,   Downloads (12 Months): 92,   Citation Count: 7
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ABSTRACT

The multi-commodity flow problem is a classical combinatorial optimization problem that addresses a number of practically important issues of congestion and bandwidth management in connection-oriented network architectures.

We consider solutions for distributed multi-commodity flow problems, which are solved by multiple agents operating in a cooperative but uncoordinated manner. We provide the first stateless greedy distributed algorithm for the concurrent multi-commodity flow problem with poly-logarithmic convergence. More precisely, our algorithm achieves (1+ε) approximation, with running time O(logP•logO(1)m•(1/ε)O(1) where P is the number of flow-paths in the network. No prior results exist for our model.

Our algorithm is a reasonable alternative to existing polynomial sequential approximation algorithms, such as Garg-Könemann [17]. The algorithm is simple and can be easily implemented or taught in a classroom.

Remarkably, our algorithm requires that the increase in the flow rate on a link is more aggressive than the decrease in the rate. Essentially all of the existing flow-control heuristics are variations of TCP, which uses a conservative cap on the increase (e.g., additive), and a rather liberal cap on the decrease (e.g., multiplicative). In contrast, our algorithm requires the increase to be multiplicative, and that this increase is dramatically more aggressive than the decrease.


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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CITED BY  7

Collaborative Colleagues:
Baruch Awerbuch: colleagues
Rohit Khandekar: colleagues