Cloud services are deployed in datacenters connected though high-bandwidth Wide Area Networks (WANs). We find that WAN traffic negatively impacts the performance of datacenter traffic, increasing tail latency by 2.5x, despite its small bandwidth demand. This behavior is caused by the long round-trip time (RTT) for WAN traffic, combined with limited buffering in datacenter switches. The long WAN RTT forces datacenter traffic to take the full burden of reacting to congestion. Furthermore, datacenter traffic changes on a faster time-scale than the WAN RTT, making it difficult for WAN congestion control to estimate available bandwidth accurately.
We present Annulus, a congestion control scheme that relies on two control loops to address these challenges. One control loop leverages existing congestion control algorithms for bottlenecks where there is only one type of traffic (i.e., WAN or datacenter). The other loop handles bottlenecks shared between WAN and datacenter traffic near the traffic source, using direct feedback from the bottleneck. We implement Annulus on a testbed and in simulation. Compared to baselines using BBR for WAN congestion control and DCTCP or DCQCN for datacenter congestion control, Annulus increases bottleneck utilization by 10% and lowers datacenter flow completion time by 1.3-3.5x.
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A Performance Evaluation of TCP BBRv2 Alpha
The alpha version of Bottleneck Bandwidth and Round-trip Time version 2 (BBRv2) has been recently presented, which aims to mitigate the shortcomings of its predecessor, BBR version 1 (BBRv1). Previous studies show that BBRv1 provides a high link utilization and low queuing delay by estimating the available bottleneck bandwidth. However, its aggressiveness induces unfairness when flows i) use different congestion control algorithms, such as CUBIC, and ii) have distinct round-trip times (RTTs). This paper presents an experimental evaluation of BBRv2, using Mininet. Results show that the coexistence between BBRv2-CUBIC is enhanced with respect to that of BBRv1-CUBIC, as measured by the fairness index. They also show that BBRv2 mitigates the RTT unfairness problem observed in BBRv1. Additionally, BBRv2 achieves a better fair share of the bandwidth than its predecessor when network conditions such as bandwidth and latency dynamically change. Results also indicate that the average flow completion time of concurrent flows is reduced when BBRv2 is used.
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- Award ID(s):
- 1925484
- NSF-PAR ID:
- 10252944
- Date Published:
- Journal Name:
- 2020 43rd International Conference on Telecommunications and Signal Processing (TSP)
- Page Range / eLocation ID:
- 309 to 312
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/TSP49548.2020.9163512
