Proctor: A Semi-Supervised Performance Anomaly Diagnosis Framework for Production HPC Systems
Thursday, July 1, 2021 3:00 PM to 3:20 PM · 20 min. (Africa/Abidjan)
Stream#4
Information
Contributors:
Abstract:
Performance variation diagnosis in High-Performance Computing (HPC) systems is a challenging problem due to the size and complexity of the systems. Application performance variation leads to premature termination of jobs, decreased energy efficiency, or wasted computing resources. Manual root-cause analysis of performance variation based on system telemetry has become an increasingly time-intensive process as it relies on human experts and the size of telemetry data has grown. Recent methods use supervised machine learning models to automatically diagnose previously encountered performance anomalies in compute nodes. However, supervised machine learning models require large labeled data sets for training. This labeled data requirement is restrictive for many real-world application domains, including HPC systems, because collecting labeled data is challenging and time-consuming, especially considering anomalies that sparsely occur. This paper proposes a novel \textit{semi-supervised framework} that diagnoses previously encountered performance anomalies in HPC systems using a limited number of labeled data points, which is more suitable for production system deployment. Our framework first learns performance anomalies' characteristics by using historical telemetry data in an unsupervised fashion. In the following process, we leverage supervised classifiers to identify anomaly types. While most semi-supervised approaches do not typically use anomalous samples, our framework takes advantage of a few labeled anomalous samples to \textit{classify} anomaly types. We evaluate our framework on a production HPC system and on a testbed HPC cluster. We show that our proposed framework achieves 60\% F1-score on average, outperforming state-of-the-art supervised methods by 11\%, and maintains an average 0.06\% anomaly miss rate.
- Omar Aaziz (Sandia National Laboratories)
- Emre Ates (Boston University)
- Jim Brandt (Sandia National Laboratories)
- Ayse K. Coskun (Boston University)
- Manuel Egele (Boston University)
- Vitus J. Leung (Sandia National Laboratories)
- Benjamin Schwaller (Sandia National Laboratories)
- Yijia Zhang (Boston University)
- Burak Aksar (Boston University)
Abstract:
Performance variation diagnosis in High-Performance Computing (HPC) systems is a challenging problem due to the size and complexity of the systems. Application performance variation leads to premature termination of jobs, decreased energy efficiency, or wasted computing resources. Manual root-cause analysis of performance variation based on system telemetry has become an increasingly time-intensive process as it relies on human experts and the size of telemetry data has grown. Recent methods use supervised machine learning models to automatically diagnose previously encountered performance anomalies in compute nodes. However, supervised machine learning models require large labeled data sets for training. This labeled data requirement is restrictive for many real-world application domains, including HPC systems, because collecting labeled data is challenging and time-consuming, especially considering anomalies that sparsely occur. This paper proposes a novel \textit{semi-supervised framework} that diagnoses previously encountered performance anomalies in HPC systems using a limited number of labeled data points, which is more suitable for production system deployment. Our framework first learns performance anomalies' characteristics by using historical telemetry data in an unsupervised fashion. In the following process, we leverage supervised classifiers to identify anomaly types. While most semi-supervised approaches do not typically use anomalous samples, our framework takes advantage of a few labeled anomalous samples to \textit{classify} anomaly types. We evaluate our framework on a production HPC system and on a testbed HPC cluster. We show that our proposed framework achieves 60\% F1-score on average, outperforming state-of-the-art supervised methods by 11\%, and maintains an average 0.06\% anomaly miss rate.
