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CLIC: An Extensible and Efficient Cross-Platform Data Analytics System
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AI
DOI：10.1109/TPDS.2023.3298038.png)
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Abstract
En 中文
With the ever-increasing data volume and application diversity, a modern data analytics job is generally built as a workflow consisting of multiple tasks. For either specific functionalities or higher performance, tasks in a workflow may need to be deployed on different data processing platforms. This article proposes CLIC, a highly extensible system for efficient cross-platform data analytics. To leverage the advantage of diverse platforms while alleviating development efforts, we propose an embedding-based operator encoding scheme and a Graph Convolutional Network model for efficient platform selection. Aiming at flexibly integrating new operators and platforms, CLIC is designed with a highly extensible system architecture that decouples the core functionalities from backend platforms. Experiments show that CLIC can significantly improve the performance of modern data analysis workflows with fast platform selection.
Keywords:
Data analysis
data processing
data systems
systems
Journal
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6
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5.2K
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