TY - GEN
T1 - BBR bufferbloat in DASH video
AU - Vargas, Santiago
AU - Drucker, Rebecca
AU - Renganathan, Aiswarya
AU - Balasubramanian, Aruna
AU - Gandhi, Anshul
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/6/3
Y1 - 2021/6/3
N2 - BBR is a new congestion control algorithm and is seeing increased adoption especially for video traffic. BBR solves the bufferbloat problem in legacy loss-based congestion control algorithms where application performance drops considerably when router buffers are deep. BBR regulates traffic such that router queues don't build up to avoid the bufferbloat problem while still maintaining high throughput. However, our analysis shows that video applications experience significantly poor performance when using BBR under deep buffers. In fact, we find that video traffic sees inflated latencies because of long queues at the router, ultimately degrading video performance. To understand this dichotomy, we study the interaction between BBR and DASH video. Our investigation reveals that BBR under deep buffers and high network burstiness severely overestimates available bandwidth and does not converge to steady state, both of which results in BBR sending substantially more data into the network, causing a queue buildup. This elevated packet sending rate under BBR is ultimately caused by the router's ability to absorb bursts in traffic, which destabilizes BBR's bandwidth estimation and overrides BBR's expected logic for exiting the startup phase. We design a new bandwidth estimation algorithm and apply it to BBR (and a still-unreleased, newer version of BBR called BBR2). Our modified BBR and BBR2 both see significantly improved video QoE even under deep buffers.
AB - BBR is a new congestion control algorithm and is seeing increased adoption especially for video traffic. BBR solves the bufferbloat problem in legacy loss-based congestion control algorithms where application performance drops considerably when router buffers are deep. BBR regulates traffic such that router queues don't build up to avoid the bufferbloat problem while still maintaining high throughput. However, our analysis shows that video applications experience significantly poor performance when using BBR under deep buffers. In fact, we find that video traffic sees inflated latencies because of long queues at the router, ultimately degrading video performance. To understand this dichotomy, we study the interaction between BBR and DASH video. Our investigation reveals that BBR under deep buffers and high network burstiness severely overestimates available bandwidth and does not converge to steady state, both of which results in BBR sending substantially more data into the network, causing a queue buildup. This elevated packet sending rate under BBR is ultimately caused by the router's ability to absorb bursts in traffic, which destabilizes BBR's bandwidth estimation and overrides BBR's expected logic for exiting the startup phase. We design a new bandwidth estimation algorithm and apply it to BBR (and a still-unreleased, newer version of BBR called BBR2). Our modified BBR and BBR2 both see significantly improved video QoE even under deep buffers.
UR - https://www.scopus.com/pages/publications/85107922501
U2 - 10.1145/3442381.3450061
DO - 10.1145/3442381.3450061
M3 - Conference contribution
AN - SCOPUS:85107922501
T3 - The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
SP - 329
EP - 341
BT - The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
PB - Association for Computing Machinery, Inc
T2 - 30th World Wide Web Conference, WWW 2021
Y2 - 19 April 2021 through 23 April 2021
ER -