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Designing overlay multicast networks for streaming
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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: Networks II table of contents
Pages: 149 - 158  
Year of Publication: 2003
ISBN:1-58113-661-7
Authors
Konstantin Andreev  Carnegie-Mellon University, Pittsburgh PA 15213.
Bruce M. Maggs  Carnegie-Mellon University, Pittsburgh PA 15213.
Adam Meyerson  Carnegie-Mellon University, Pittsburgh PA 15213.
Ramesh K. Sitaraman  Akamai Technologies Inc., Cambridge MA & University of Massachusetts, Amherst MA
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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Downloads (6 Weeks): 4,   Downloads (12 Months): 36,   Citation Count: 4
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ABSTRACT

In this paper we present a polynomial time approximation algorithm for designing a multicast overlay network. The algorithm finds a solution that satisfies capacity and reliability constraints to within a constant factor of optimal, and cost to within alogarithmic factor. The class of networks that our algorithm applies to includes the one used by Akamai Technologies to deliver live media streams over the Internet. In particular, we analyze networks consisting of three stages of nodes. The nodes in the first stage are the sources where live streams originate. A source forwards each of its streams to one or more nodes in the second stage, which are called reflectors. A reflector can split an incoming stream into multiple identical outgoing streams, which are then sent on to nodes in the third and final stage, which are called the sinks. As the packets in a stream trave from one stage to the next, some of them may be lost. The job of a sink is to combine the packets from multiple instances of the same stream (by reordering packets and discarding duplicates) to form a single instance of the stream with minimal loss. We assume that the loss rate between any pair of nodes in the network is known, and that losses between different pairs are independent, but discuss extensions in which some losses may be correlated.


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:
Konstantin Andreev: colleagues
Bruce M. Maggs: colleagues
Adam Meyerson: colleagues
Ramesh K. Sitaraman: colleagues