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Methodological frameworks for large-scale network analysis and design

Papachristodoulou, Antonis and Li, Lun and Doyle, John C. (2004) Methodological frameworks for large-scale network analysis and design. ACM SIGCOMM Computer Communication Review, 34 (3). pp. 7-20. ISSN 0146-4833. doi:10.1145/1031134.1031138.

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This paper emphasizes the need for methodological frameworks for analysis and design of large scale networks which are independent of specific design innovations and their advocacy, with the aim of making networking a more systematic engineering discipline. Networking problems have largely confounded existing theory, and innovation based on intuition has dominated design. This paper will illustrate potential pitfalls of this practice. The general aim is to illustrate universal aspects of theoretical and methodological research that can be applied to network design and verification. The issues focused on will include the choice of models, including the relationship between flow and packet level descriptions, the need to account for uncertainty generated by modelling abstractions, and the challenges of dealing with network scale. The rigorous comparison of proposed schemes will be illustrated using various abstractions. While standard tools from robust control theory have been applied in this area, we will also illustrate how network-specific challenges can drive the development of new mathematics that expand their range of applicability, and how many enormous challenges remain.

Item Type:Article
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Doyle, John C.0000-0002-1828-2486
Additional Information:© 2004 ACM.
Issue or Number:3
Record Number:CaltechAUTHORS:20161207-172521946
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Official Citation:Antonis Papachristodoulou, Lun Li, and John C. Doyle. 2004. Methodological frameworks for large-scale network analysis and design. SIGCOMM Comput. Commun. Rev. 34, 3 (July 2004), 7-20. DOI=
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72647
Deposited On:08 Dec 2016 03:49
Last Modified:11 Nov 2021 05:04

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