An Experimental Study of the Learnability of Congestion Control
Name
Balakrishnan_An Experimental Study.pdf
Size
504.25 KB
Format
Adobe PDF
Checksum (MD5)
bdd9f0edd722d43a77ec3c0baf675842
Author(s)
Sivaraman Kaushalram, Anirudh
Winstein, Keith
Thaker, Pratiksha R.
Balakrishnan, Hari
Date Issued
August 2014
Journal
Proceedings of the 2014 ACM conference on SIGCOMM
Publisher
Association for Computing Machinery (ACM)
Citation
Sivaraman, Anirudh, Keith Winstein, Pratiksha Thanker, and Hari Balakrishnan. "An Experimental Study of the Learnability of Congestion Control." Proceedings of the 2014 ACM conference on SIGCOMM, August 17-22, 2014, Chicago, IL.
Version
Author's final manuscript
Abstract
When designing a distributed network protocol, typically it is infeasible to fully define the target network where the protocol is intended to be used. It is therefore natural to ask: How faithfully do protocol designers really need to understand the networks they design for? What are the important signals that endpoints should listen to? How can researchers gain confidence that systems that work well on well-characterized test networks during development will also perform adequately on real networks that are inevitably more complex, or future networks yet to be developed? Is there a tradeoff between the performance of a protocol and the breadth of its intended operating range of networks? What is the cost of playing fairly with cross-traffic that is governed by another protocol? We examine these questions quantitatively in the context of congestion control, by using an automated protocol-design tool to approximate the best possible congestion-control scheme given imperfect prior knowledge about the network. We found only weak evidence of a tradeoff between operating range in link speeds and performance, even when the operating range was extended to cover a thousand-fold range of link speeds. We found that it may be acceptable to simplify some characteristics of the network—such as its topology—when modeling for design purposes. Some other features, such as the degree of multiplexing and the aggressiveness of contending endpoints, are important to capture in a model.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://cs.stanford.edu/~keithw/www/Learnability-SIGCOMM2014.pdf
