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Online oblivious routing
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Source ACM Symposium on Parallel Algorithms and Architectures archive
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures table of contents
San Diego, California, USA
SESSION: Routing I table of contents
Pages: 44 - 49  
Year of Publication: 2003
ISBN:1-58113-661-7
Authors
Nikhil Bansal  Carnegie Mellon University, Pittsburgh, PA
Avrim Blum  Carnegie Mellon University, Pittsburgh, PA
Shuchi Chawla  Carnegie Mellon University, Pittsburgh, PA
Adam Meyerson  Carnegie Mellon University, Pittsburgh, PA
Sponsors
ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
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ABSTRACT

We consider an online version of the oblivious routing problem. Oblivious routing is the problem of picking a routing between each pair of nodes (or a set of flows), without knowledge of the traffic or demand between each pair, with the goal of minimizing the maximum congestion on any edge in the graph. In the online version of the problem, we consider a "repeated game" setting, in which the algorithm is allowed to choose a new routing each night, but is still oblivious to the demands that will occur the next day. The cost of the algorithm at every time step is its competitive ratio, or the ratio of its congestion to the minimum possible congestion for the demands at that time step.We present an algorithm that is (1+ε) competitive with respect to the best algorithm that uses a single routing for the entire sequence of days (known as the optimal static routing). Our result is a strengthening of the recent result of Azar et al [4], who gave a polynomial time algorithm to find an oblivious routing with the best possible competitive ratio, in that our algorithm achieves a competitive ratio arbitrarily to close to that of Azar et al [4], while at the same time performing nearly as well as the optimal static routing for the given sequence of demands. Our work was done independently, but subsequent to that of Azar et al [4].


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:
Nikhil Bansal: colleagues
Avrim Blum: colleagues
Shuchi Chawla: colleagues
Adam Meyerson: colleagues