MLEFlow: Learning from History to Improve Load Balancing in Tor

Tor has millions of daily users seeking privacy while browsing the Internet. It has thousands of relays to route users’ packets while anonymizing their sources and destinations. Users choose relays to forward their traffic according to probability distributions published by the Tor authorities. The...

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Autores principales:	Darir Hussein, Sibai Hussein, Cheng Chin-Yu, Borisov Nikita, Dullerud Geir, Mitra Sayan
Formato:	article
Lenguaje:	EN
Publicado:	Sciendo 2022
Materias:	tor capacity estimation load balancing maximum likelihood estimation shadow simulator privacy Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/690025189b764d579b9f7d70cb4156d2
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id	oai:doaj.org-article:690025189b764d579b9f7d70cb4156d2
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spelling	oai:doaj.org-article:690025189b764d579b9f7d70cb4156d22021-12-05T14:11:10ZMLEFlow: Learning from History to Improve Load Balancing in Tor2299-098410.2478/popets-2022-0005https://doaj.org/article/690025189b764d579b9f7d70cb4156d22022-01-01T00:00:00Zhttps://doi.org/10.2478/popets-2022-0005https://doaj.org/toc/2299-0984Tor has millions of daily users seeking privacy while browsing the Internet. It has thousands of relays to route users’ packets while anonymizing their sources and destinations. Users choose relays to forward their traffic according to probability distributions published by the Tor authorities. The authorities generate these probability distributions based on estimates of the capacities of the relays. They compute these estimates based on the bandwidths of probes sent to the relays. These estimates are necessary for better load balancing. Unfortunately, current methods fall short of providing accurate estimates leaving the network underutilized and its capacities unfairly distributed between the users’ paths. We present MLEFlow, a maximum likelihood approach for estimating relay capacities for optimal load balancing in Tor. We show that MLEFlow generalizes a version of Tor capacity estimation, TorFlow-P, by making better use of measurement history. We prove that the mean of our estimate converges to a small interval around the actual capacities, while the variance converges to zero. We present two versions of MLEFlow: MLEFlow-CF, a closed-form approximation of the MLE and MLEFlow-Q, a discretization and iterative approximation of the MLE which can account for noisy observations. We demonstrate the practical benefits of MLEFlow by simulating it using a flow-based Python simulator of a full Tor network and packet-based Shadow simulation of a scaled down version. In our simulations MLEFlow provides significantly more accurate estimates, which result in improved user performance, with median download speeds increasing by 30%.Darir HusseinSibai HusseinCheng Chin-YuBorisov NikitaDullerud GeirMitra SayanSciendoarticletorcapacity estimationload balancingmaximum likelihood estimationshadow simulatorprivacyEthicsBJ1-1725Electronic computers. Computer scienceQA75.5-76.95ENProceedings on Privacy Enhancing Technologies, Vol 2022, Iss 1, Pp 75-104 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	tor capacity estimation load balancing maximum likelihood estimation shadow simulator privacy Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95
spellingShingle	tor capacity estimation load balancing maximum likelihood estimation shadow simulator privacy Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95 Darir Hussein Sibai Hussein Cheng Chin-Yu Borisov Nikita Dullerud Geir Mitra Sayan MLEFlow: Learning from History to Improve Load Balancing in Tor
description	Tor has millions of daily users seeking privacy while browsing the Internet. It has thousands of relays to route users’ packets while anonymizing their sources and destinations. Users choose relays to forward their traffic according to probability distributions published by the Tor authorities. The authorities generate these probability distributions based on estimates of the capacities of the relays. They compute these estimates based on the bandwidths of probes sent to the relays. These estimates are necessary for better load balancing. Unfortunately, current methods fall short of providing accurate estimates leaving the network underutilized and its capacities unfairly distributed between the users’ paths. We present MLEFlow, a maximum likelihood approach for estimating relay capacities for optimal load balancing in Tor. We show that MLEFlow generalizes a version of Tor capacity estimation, TorFlow-P, by making better use of measurement history. We prove that the mean of our estimate converges to a small interval around the actual capacities, while the variance converges to zero. We present two versions of MLEFlow: MLEFlow-CF, a closed-form approximation of the MLE and MLEFlow-Q, a discretization and iterative approximation of the MLE which can account for noisy observations. We demonstrate the practical benefits of MLEFlow by simulating it using a flow-based Python simulator of a full Tor network and packet-based Shadow simulation of a scaled down version. In our simulations MLEFlow provides significantly more accurate estimates, which result in improved user performance, with median download speeds increasing by 30%.
format	article
author	Darir Hussein Sibai Hussein Cheng Chin-Yu Borisov Nikita Dullerud Geir Mitra Sayan
author_facet	Darir Hussein Sibai Hussein Cheng Chin-Yu Borisov Nikita Dullerud Geir Mitra Sayan
author_sort	Darir Hussein
title	MLEFlow: Learning from History to Improve Load Balancing in Tor
title_short	MLEFlow: Learning from History to Improve Load Balancing in Tor
title_full	MLEFlow: Learning from History to Improve Load Balancing in Tor
title_fullStr	MLEFlow: Learning from History to Improve Load Balancing in Tor
title_full_unstemmed	MLEFlow: Learning from History to Improve Load Balancing in Tor
title_sort	mleflow: learning from history to improve load balancing in tor
publisher	Sciendo
publishDate	2022
url	https://doaj.org/article/690025189b764d579b9f7d70cb4156d2
work_keys_str_mv	AT darirhussein mleflowlearningfromhistorytoimproveloadbalancingintor AT sibaihussein mleflowlearningfromhistorytoimproveloadbalancingintor AT chengchinyu mleflowlearningfromhistorytoimproveloadbalancingintor AT borisovnikita mleflowlearningfromhistorytoimproveloadbalancingintor AT dullerudgeir mleflowlearningfromhistorytoimproveloadbalancingintor AT mitrasayan mleflowlearningfromhistorytoimproveloadbalancingintor
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MLEFlow: Learning from History to Improve Load Balancing in Tor

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