Vidal's libraryTitle: | Impact of Problem Centralization in Distributed Constraint Optimization Algorithms |
Author: | John Davin and Pragnesh Jay Modi |
Book Tittle: | Proceedings of the Fourth International Joint Conference on Autonomous Agents and MultiAgent Systems |
Pages: | 1057--1066 |
Year: | 2005 |
Crossref: | aamas05 |
Abstract: | Recent progress in Distributed Constraint Optimization Problems (DCOP) has led to a range of algorithms now available which differ in their amount of problem centralization. Problem centralization can have a significant impact on the amount of computation required by an agent but unfortunately the dominant evaluation metric of “number of cycles” fails to account for this cost. We analyze the relative performance of two recent algorithms for DCOP: OptAPO, which performs partial centralization, and Adopt, which maintains distribution of the DCOP. Previous comparison of Adopt and OptAPO has found that OptAPO requires fewer cycles than Adopt. We extend the cycles metric to define “Cycle-Based Runtime (CBR)” to account for both the amount of computation required in each cycle and the communication latency between cycles. Using the CBR metric, we show that Adopt outperforms OptAPO under a range of communication latencies. We also ask: What level of centralization is most suitable for a given communication latency? We use CBR to create performance curves for three algorithms that vary in degree of centralization, namely Adopt, OptAPO, and centralized Branch and Bound search. |
Cited by 27 - Google Scholar
@InProceedings{davin05a,
author = {John Davin and Pragnesh Jay Modi},
title = {Impact of Problem Centralization in Distributed
Constraint Optimization Algorithms},
booktitle = {Proceedings of the Fourth International Joint
Conference on Autonomous Agents and MultiAgent
Systems},
crossref = {aamas05},
pages = {1057--1066},
year = 2005,
abstract = {Recent progress in Distributed Constraint
Optimization Problems (DCOP) has led to a range of
algorithms now available which differ in their
amount of problem centralization. Problem
centralization can have a significant impact on the
amount of computation required by an agent but
unfortunately the dominant evaluation metric of
``number of cycles'' fails to account for this
cost. We analyze the relative performance of two
recent algorithms for DCOP: OptAPO, which performs
partial centralization, and Adopt, which maintains
distribution of the DCOP. Previous comparison of
Adopt and OptAPO has found that OptAPO requires
fewer cycles than Adopt. We extend the cycles metric
to define ``Cycle-Based Runtime (CBR)'' to account
for both the amount of computation required in each
cycle and the communication latency between
cycles. Using the CBR metric, we show that Adopt
outperforms OptAPO under a range of communication
latencies. We also ask: What level of centralization
is most suitable for a given communication latency?
We use CBR to create performance curves for three
algorithms that vary in degree of centralization,
namely Adopt, OptAPO, and centralized Branch and
Bound search.},
keywords = {multiagent distributed-search dcop},
url = {http://jmvidal.cse.sc.edu/library/davin05a.pdf},
googleid = {m23paRkdgGIJ:scholar.google.com/},
cluster = {7097705007724195227}
}
Last modified: Wed Mar 9 10:16:27 EST 2011