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Computing separable functions via gossip
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Source Annual ACM Symposium on Principles of Distributed Computing archive
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing table of contents
Denver, Colorado, USA
SESSION: Peer-to-peer table of contents
Pages: 113 - 122  
Year of Publication: 2006
ISBN:1-59593-384-0
Authors
Damon Mosk-Aoyama  Stanford University, Stanford, CA
Devavrat Shah  Massachusetts Institute of Technology, Cambridge, MA
Sponsors
ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 14,   Downloads (12 Months): 101,   Citation Count: 11
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ABSTRACT

Motivated by applications to sensor, peer-to-peer, and ad-hoc networks, we study the problem of computing functions of values at the nodes in a network in a totally distributed manner. In particular, we consider separable functions, which can be written as linear combinations of functions of individual variables. Known iterative algorithms for averaging can be used to compute the normalized values of such functions, but these algorithms do not extend in general to the computation of the actual values of separable functions.The main contribution of this paper is the design of a distributed randomized algorithm for computing separable functions based on properties of exponential random variables. We bound the running time of our algorithm in terms of the running time of an information spreading algorithm used as a subroutine by the algorithm. Since we are interested in totally distributed algorithms, we consider a randomized gossip mechanism for information spreading as the subroutine. Combining these algorithms yields a complete and simple distributed algorithm for computing separable functions.The second contribution of this paper is an analysis of the information spreading time of the gossip algorithm. This analysis yields an upper bound on the information spreading time, and therefore a corresponding upper bound on the running time of the algorithm for computing separable functions, in terms of the conductance of an appropriate stochastic matrix. These bounds imply that, for a class of graphs with small spectral gap (such as grid graphs), the time used by our algorithm to compute averages is of a smaller order than the time required for the computation of averages by a known iterative gossip scheme [5].


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  11

Collaborative Colleagues:
Damon Mosk-Aoyama: colleagues
Devavrat Shah: colleagues