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- W4384705400 abstract "Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast and cost-efficient execution of serverless ML workflows (specially for hyperparameter tuning and model training). Unfortunately, existing resource allocation methods are static, treat functions equally, and rely on offline prediction, which limit their efficiency. In this paper, we introduce CE-scaling – a Cost-Efficient autoscaling framework for serverless ML work-flows. During the hyperparameter tuning, CE-scaling partitions resources across stages according to their exact usage to minimize resource waste. Moreover, it incorporates an online prediction method to dynamically adjust resources during model training. We implement and evaluate CE-scaling on AWS Lambda using various ML models. Evaluation results show that compared to state-of-the-art static resource allocation methods, CE-scaling can reduce the job completion time and the monetary cost by up to 63% and 41% for hyperparameter tuning, respectively; and by up to 58% and 38% for model training." @default.
- W4384705400 created "2023-07-20" @default.
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- W4384705400 date "2023-05-01" @default.
- W4384705400 modified "2023-09-25" @default.
- W4384705400 title "QoS-Aware and Cost-Efficient Dynamic Resource Allocation for Serverless ML Workflows" @default.
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- W4384705400 doi "https://doi.org/10.1109/ipdps54959.2023.00093" @default.
- W4384705400 hasPublicationYear "2023" @default.
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