by Yuriy Brun, Nenad Medvidovic
Abstract:
Large networks, such as the Internet, pose an ideal medium for solving computationally intensive problems, such as NP-complete problems, yet no well-scaling architecture for computational Internet-sized systems exists. We propose a software architectural style for large networks, based on a formal mathematical study of crystal growth that will exhibit properties of (1) discreetness (nodes on the network cannot learn the algorithm or input of the computation), (2) fault-tolerance (malicious, faulty, and unstable nodes may not break the computation), and (3) scalability (communication among the nodes does not increase with network or problem size).
Citation:
Yuriy Brun and Nenad Medvidovic, An architectural style for solving computationally intensive problems on large networks, in Proceedings of Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2007 (SEAMS 2020 Most Influential Paper Award).
Bibtex:
@inproceedings{Brun07seams,
author = {Yuriy Brun and Nenad Medvidovic},
title = {\href{http://people.cs.umass.edu/brun/pubs/pubs/Brun07seams.pdf}{An
architectural style for solving computationally intensive problems on large
networks}},
booktitle = {Proceedings of Software Engineering for Adaptive and
Self-Managing Systems (SEAMS)},
venue = {SEAMS},
month = {May},
date = {26--27},
year = {2007},
address = {Minneapolis, {MN}, {USA}},
doi = {10.1109/SEAMS.2007.4},
accept = {$\frac{18}{26} \approx 69\%$},
note = {\raisebox{-.5ex}{\includegraphics[height=2.5ex]{trophy}}~SEAMS 2020 Most Influential Paper Award.
\href{https://doi.org/10.1109/SEAMS.2007.4}{DOI: 10.1109/SEAMS.2007.4}},
comment = {<span class="emphasis">SEAMS 2020 Most Influential Paper Award</span>},
abstract = {Large networks, such as the Internet, pose an ideal medium for
solving computationally intensive problems, such as NP-complete problems, yet
no well-scaling architecture for computational Internet-sized systems exists.
We propose a software architectural style for large networks, based on a
formal mathematical study of crystal growth that will exhibit properties of
(1) discreetness (nodes on the network cannot learn the algorithm or input of
the computation), (2) fault-tolerance (malicious, faulty, and unstable nodes
may not break the computation), and (3) scalability (communication among the
nodes does not increase with network or problem size).},
fundedBy = {NSF ITR-0312780},
}