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Optimal plans for aggregation
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Source Annual ACM Symposium on Principles of Distributed Computing archive
Proceedings of the twenty-first annual symposium on Principles of distributed computing table of contents
Monterey, California
SESSION: Session 5 table of contents
Pages: 144 - 152  
Year of Publication: 2002
ISBN:1-58113-485-1
Authors
Andrei Broder  IBM Research Division
Michael Mitzenmacher  Harvard University
Sponsors
SIGOPS: ACM Special Interest Group on Operating Systems
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
Publisher
ACM  New York, NY, USA
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ABSTRACT

We consider the following problem, which arises in the context of distributed Web computations. An aggregator aims to combine specific data from n sources. The aggregator contacts all sources at once. The time for each source to return its data to the aggregator is independent and identically distributed according to a known distribution. The aggregator at some point stops waiting for data and returns an answer depending only on the data received so far. If the aggregator returns the aggregated information from k of the n sources at time t it obtains a reward Rk(t) that grows with k and decreases with t. The goal of the aggregator is to maximize its expected reward.We prove that for certain broad families of distributions and broad classes of reward functions, the optimal plan for the aggregator has a simple form and hence can be easily computed.


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:
Andrei Broder: colleagues
Michael Mitzenmacher: colleagues