TY - GEN
T1 - Enabling Users to Control their Internet
AU - Tahir, Ammar
AU - Mittal, Radhika
N1 - Publisher Copyright:
© NSDI 2023.All rights reserved
PY - 2023
Y1 - 2023
N2 - Access link from the ISP tends to be the bottleneck for many users. However, users today have no control over how the access bandwidth (which is under the ISP's control) is divided across their incoming flows. In this paper, we present a system, CRAB, that runs at the receiver's devices - home routers and endpoints - and enforces user-specified weights across the incoming flows, without any explicit support from the ISP or the senders. It involves a novel control loop that continuously estimates available downlink capacity and flow demands by observing the incoming traffic, computes the max-min weighted fair share rates for the flows using these estimates, and throttles the flows to the computed rates. The key challenge that CRAB must tackle is that the demand and capacity estimated by observing the incoming traffic at the receiver (after the bottleneck) is inherently ambiguous - CRAB's control loop is designed to effectively avoid and correct these ambiguities. We implement CRAB on a Linux machine and Linksys WRT3200ACM home router. Our evaluation, involving real-world flows, shows how CRAB can enforce user preferences to achieve 2× lower web page load times and 3× higher video quality than the status quo.
AB - Access link from the ISP tends to be the bottleneck for many users. However, users today have no control over how the access bandwidth (which is under the ISP's control) is divided across their incoming flows. In this paper, we present a system, CRAB, that runs at the receiver's devices - home routers and endpoints - and enforces user-specified weights across the incoming flows, without any explicit support from the ISP or the senders. It involves a novel control loop that continuously estimates available downlink capacity and flow demands by observing the incoming traffic, computes the max-min weighted fair share rates for the flows using these estimates, and throttles the flows to the computed rates. The key challenge that CRAB must tackle is that the demand and capacity estimated by observing the incoming traffic at the receiver (after the bottleneck) is inherently ambiguous - CRAB's control loop is designed to effectively avoid and correct these ambiguities. We implement CRAB on a Linux machine and Linksys WRT3200ACM home router. Our evaluation, involving real-world flows, shows how CRAB can enforce user preferences to achieve 2× lower web page load times and 3× higher video quality than the status quo.
UR - http://www.scopus.com/inward/record.url?scp=85159297932&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85159297932&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85159297932
T3 - Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
SP - 555
EP - 573
BT - Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
PB - USENIX Association
T2 - 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
Y2 - 17 April 2023 through 19 April 2023
ER -