Uber's Ceilometer: Benchmarking Beyond Application Metrics

Decoding Uber's Infrastructure Insights

Uber's Ceilometer is a compelling solution for infrastructure benchmarking, offering a centralized platform that automates the traditionally fragmented process. The innovation lies in its ability to provide consistent, data-driven performance signals across diverse environments, including different cloud providers and hardware generations. The framework's support for a wide range of workload types, from synthetic benchmarks to stateful database workloads, is particularly noteworthy. The integration with existing platforms like Odin and Ballast suggests a well-integrated approach. However, the article lacks deep technical specifics on the inner workings of Ceilometer, such as the exact architecture of its distributed system or the algorithms used for result validation and normalization. While it mentions AI/ML integration plans, the current state of these features isn't detailed, leaving room for speculation about their impact and effectiveness. Furthermore, the article doesn't delve into the challenges encountered during Ceilometer's development and deployment, such as data volume handling, benchmark design optimization, or the intricacies of correlating performance across varied workloads and hardware configurations. More detail on these aspects would provide a more complete picture of the framework's practical application and potential limitations. The article's focus on Uber's internal use case also limits its immediate applicability to other organizations. While the concepts are transferable, the specific tooling and integration details might require significant adaptation.

Key Points

• Ceilometer is an internal adaptive benchmarking framework developed by Uber to evaluate infrastructure performance beyond application-level metrics.

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📖 Source: Benchmarking Beyond the Application Layer: How Uber Evaluates Infrastructure Changes and Cloud Skus