According to @JeffDean, the influential 'The Tail at Scale' paper co-authored with @labarroso has been honored with the SIGOPS Hall of Fame award for its significant impact on distributed systems performance at scale (source: https://twitter.com/JeffDean/status/1978497327166845130). The paper, originally published in 2013, analyzes tail latency—the slowest response times in large-scale computing environments such as those deployed by Google. It identifies the business-critical challenge of latency spikes in AI-driven and cloud-based services, where a single slow server can dramatically degrade user experience. The authors introduced practical techniques like tied requests and hedged requests to mitigate latency variability, directly relevant for optimizing AI inference and training pipelines that rely on distributed computing (source: https://research.google/pubs/the-tail-at-scale/). Their work continues to inform architecture and operational strategies for AI platforms, making it essential reading for developers and CTOs building scalable, reliable AI systems (source: https://www.sigops.org/awards/hof/).