Unbiased Experiments in Congested Networks (2110.00118v1)

Abstract: When developing a new networking algorithm, it is established practice to run a randomized experiment, or A/B test, to evaluate its performance. In an A/B test, traffic is randomly allocated between a treatment group, which uses the new algorithm, and a control group, which uses the existing algorithm. However, because networks are congested, both treatment and control traffic compete against each other for resources in a way that biases the outcome of these tests. This bias can have a surprisingly large effect; for example, in lab A/B tests with two widely used congestion control algorithms, the treatment appeared to deliver 150% higher throughput when used by a few flows, and 75% lower throughput when used by most flows-despite the fact that the two algorithms have identical throughput when used by all traffic. Beyond the lab, we show that A/B tests can also be biased at scale. In an experiment run in cooperation with Netflix, estimates from A/B tests mistake the direction of change of some metrics, miss changes in other metrics, and overestimate the size of effects. We propose alternative experiment designs, previously used in online platforms, to more accurately evaluate new algorithms and allow experimenters to better understand the impact of congestion on their tests.

• The paper reveals significant biases in standard A/B testing methodologies when applied to congested network environments due to interference between treatment and control groups.
• A key finding is that interference can drastically skew results, showing a misleading 150% throughput increase in a lab setting that becomes a 75% decrease at scale.
• To counter these biases, the study proposes alternative experimental designs like switchback tests and gradual deployments that better account for real-world network dynamics.

Unbiased Experiments in Congested Networks: A Critical Insight into A/B Testing Limitations

The paper "Unbiased Experiments in Congested Networks" provides a rigorous examination of the biases inherent in A/B testing methodologies, particularly in the context of congested network environments. The researchers critically assess traditional A/B testing frameworks, highlighting significant limitations, especially interference issues when control and treatment groups share congested network resources.

Core Findings and Methodology

The authors illustrate that standard A/B testing procedures fail to account for the interference between treatment and control groups in network environments. This interference, particularly prevalent in congested networks, can skew test results, significantly misrepresenting the efficacy of new algorithms. For instance, in their experiments, a new congestion control algorithm demonstrated a misleading 150% increase in throughput in a controlled environment when only a few flows were treated, yet this outcome dramatically shifted to a 75% decrease in throughput when scaled to treat most flows.

A notable numerical highlight presented in the paper is the scale of error in standard lab A/B tests verses real-world applications observed at Netflix, which suggest incorrect metric directions and inaccurate change estimates in network performance and user experience. This underlines the practical need for adjusted testing methods that cater to scale and interference issues in large-scale networks.

Interference and Experimental Design

A major contribution of this paper is its in-depth analysis of interference in A/B testing. The researchers emphasize that in congested networks, treatment and control groups often compete for the same resources, leading to interference that biases the experimental results. This phenomenon can lead to incorrect conclusions about the effectiveness of new algorithms when evaluated using standard A/B test designs.

To address these limitations, the paper proposes alternative experimental designs like switchback experiments, which allow for periodic switching between treatment and control over time, thus mitigating interference effects. The findings advocate for an adaptive approach in experiment design, utilizing gradual deployment experiments to gauge interaction effects before large-scale algorithm rollout.

Implications and Future Directions

The implications of the research extend beyond theoretical insights into practical applications, particularly in network resource management and the deployment of new internet protocols. By demonstrating that interference can radically alter perceived algorithm efficacy, the paper suggests a reevaluation of how large-scale tech companies conduct network-related A/B tests. This insight is pivotal for practitioners aiming to implement algorithms that perform reliably under real-world conditions, akin to environments at platforms like Netflix.

On a theoretical level, the paper opens new avenues for designing experiments that can accommodate network-induced biases, prompting further research into causal inference in network settings. The paper hints at future developments where network algorithms might be evaluated through advanced experiment designs that explicitly account for interference, overcoming traditional limitations evident in A/B testing methodologies.

Conclusion

Through comprehensive experimentation and analysis, this paper reveals the flaws of conventional A/B testing in congested network environments, urging the adoption of more nuanced experimental designs. The findings stress the importance of understanding and quantifying interference effects to ensure accurate evaluations of new network algorithms, ensuring innovations are deployed with clear expectations of their performance and contribution in dynamic network settings. This research emphasizes the evolving landscape of network testing and the need for constant adaptation of experimental frameworks to maintain alignment with real-world operational contexts.