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End-to-End Transmission Control by Modeling Uncertainty about the Network State

Author(s)
Winstein, Keith J.; Balakrishnan, Hari
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Abstract
This paper argues that the bar for the incorporation of a new subnetwork or link technology in the current Internet is much more than the ability to send minimum-sized IP packets: success requires that TCP perform well over any subnetwork. This requirement imposes a number of additional constraints, some hard to meet because TCP’s network model is limited and its overall objective challenging to specify precisely. As a result, network evolution has been hampered and the potential of new subnetwork technologies has not been realized in practice. The poor end-to-end performance of many important subnetworks, such as wide-area cellular networks that zealously hide non-congestive losses and introduce enormous delays as a result, or home broadband networks that suffer from the notorious “bufferbloat” problem, are symptoms of this more general issue. We propose an alternate architecture for end-to-end resource management and transmission control, in which the endpoints work directly to achieve a specified goal. Each endpoint treats the network as an nondeterministic automaton whose parameters and topology are uncertain. The endpoint maintains a probability distribution on what it thinks the network’s configuration may be. At each moment, the endpoint acts to maximize the expected value of a utility function that is given explicitly. We present preliminary simulation results arguing that the approach is tractable and holds promise.
Date issued
2011-11
URI
http://hdl.handle.net/1721.1/67032
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of the Tenth ACM Workshop on Hot Topics in Networks (Hotnets X)
Publisher
Association for Computing Machinery
Citation
Winstein, Keith and Hari Balakrishnan. "End-to-End Transmission Control by Modeling Uncertainty about the Network State." In: Proceedings of the Tenth ACM Workshop on Hot Topics in Networks (Hotnets X), November 14-15, 2011, Cambridge, Mass.
Version: Author's final manuscript
ISBN
978-1-4503-1059-8

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