DWT: Decoupled Workload Tracing for Data Centers

2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)(2020)

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Workload tracing is the foundational technology that many applications hinge upon. However, recent paradigm shift to-ward cloud computing has caused tremendous challenges to traditional workload tracing. Existing solutions either require a dedicated offline cluster or fail to capture the full-spectrum workload characteristics. This paper proposes DWT, a novel framework that leverages fast online instruction tracing, and uses synthetic data offline for memory access pattern reconstruction, thereby capturing the full workload characteristics while obviating the need of dedicated clusters. Experiment results show that the stack distance profiles generated from synthetic address traces match well with the original ones across all SPEC CPU 2017 programs and representative cloud applications, with correlation coefficient R^2 no less than 0.9. The page-level access frequencies also match well with those of the original programs. This decoupled tracing approach not only removes the roadblocks on workload characterization for data centers, but also enables new applications such as efficient online resource management.
datacenter workload tracing,performance modeling,memory access patterns,synthetic data
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