Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929274691256320 |
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| author | Will, Jonathan Scheinert, Dominik Bode, Jan Kring, Cedric Zunzer, Seraphin Thamsen, Lauritz |
| author_facet | Will, Jonathan Scheinert, Dominik Bode, Jan Kring, Cedric Zunzer, Seraphin Thamsen, Lauritz |
| contents | Performance modeling for large-scale data analytics workloads can improve the efficiency of cluster resource allocations and job scheduling. However, the performance of these workloads is influenced by numerous factors, such as job inputs and the assigned cluster resources. As a result, performance models require significant amounts of training data. This data can be obtained by exchanging runtime metrics between collaborating organizations. Yet, not all organizations may be inclined to publicly disclose such metadata.
We present a privacy-preserving approach for sharing runtime metrics based on differential privacy and data synthesis. Our evaluation on performance data from 736 Spark job executions indicates that fully anonymized training data largely maintains performance prediction accuracy, particularly when there is minimal original data available. With 30 or fewer available original data samples, the use of synthetic training data resulted only in a one percent reduction in performance model accuracy on average. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05692 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling Will, Jonathan Scheinert, Dominik Bode, Jan Kring, Cedric Zunzer, Seraphin Thamsen, Lauritz Distributed, Parallel, and Cluster Computing Performance modeling for large-scale data analytics workloads can improve the efficiency of cluster resource allocations and job scheduling. However, the performance of these workloads is influenced by numerous factors, such as job inputs and the assigned cluster resources. As a result, performance models require significant amounts of training data. This data can be obtained by exchanging runtime metrics between collaborating organizations. Yet, not all organizations may be inclined to publicly disclose such metadata. We present a privacy-preserving approach for sharing runtime metrics based on differential privacy and data synthesis. Our evaluation on performance data from 736 Spark job executions indicates that fully anonymized training data largely maintains performance prediction accuracy, particularly when there is minimal original data available. With 30 or fewer available original data samples, the use of synthetic training data resulted only in a one percent reduction in performance model accuracy on average. |
| title | Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2403.05692 |