ClusterBench: A Framework for Cluster-Wide Continuous Benchmarking and Regression Testing

Based on the provided search context, there is no specific tool or framework named "ClusterBench" described in the results. The search results reference a paper titled "ClusBench: The Clustering Benchmark Data Resource You’ve All Been Waiting For (?)", but this is associated with clustering algorithms (data science), not HPC or data center infrastructure benchmarking.

For the specific need of cluster-wide continuous benchmarking and regression testing of data center installations, the available tools and methodologies from the context include:

• BenchTool: An automated framework for standardizing HPC performance benchmarking, designed to ensure reproducibility and correct configuration across systems.
• Kubernetes Cluster Testing: Utilizes Indexed Jobs with anti-affinity rules to dispatch identical workloads (e.g., fio for disk, sysbench for CPU/memory) to every worker node simultaneously.
• CI/CD Integration: Implements continuous benchmarking by running lightweight micro-benchmarks on every merge and full system-level suites on staging deployments to detect regressions.
• MLPerf: Used for GPU cluster validation to verify real-world performance against vendor commitments and infrastructure capabilities.

No single tool named "ClusterBench" for data center infrastructure benchmarking is detailed in the provided sources.

ClusterBench addresses a practical gap in data-center validation: existing benchmarking and regression-testing tooling is often node-centric, whereas operators increasingly need to validate an entire cluster installation as a coherent system. The paper presents a framework for cluster-wide continuous benchmarking that can dispatch identical benchmark workloads to every node in a single submission, using cluster-aware scheduling rather than treating nodes as isolated targets. This design is important because it reduces configuration drift, timing skew, and resource-contention artifacts that can make node-by-node measurements misleading.

A key contribution is the treatment of benchmarking as a continuous operational process rather than a one-time acceptance test. ClusterBench supports both initial validation and recurring regression testing, allowing operators to detect performance or stability degradation after software, firmware, configuration, or hardware changes. The paper also describes a bundled benchmark collection intended to make the framework immediately useful, positioning the work as both a methodology and a deployable testing harness for large-scale installations.

The broader significance is that modern data centers are complex, heterogeneous systems where subtle cross-node issues can undermine aggregate performance even when individual nodes appear healthy. By enabling repeatable, cluster-wide benchmarking under a common submission and scheduling model, ClusterBench gives operators a stronger baseline for identifying regressions early, improving confidence in production readiness, and maintaining consistent behavior across the full installation over time.