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2020 IEEE 13th International Conference on Cloud Computing (CLOUD)
Understanding the resource usage behaviors of the ever-increasing machine learning workloads are critical to cloud providers offering Machine Learning (ML) services. Capable of auto-scaling resources for customer workloads can significantly improve resource utilization, thus greatly reducing the cost. Here we leverage the AI4DL framework  to characterize workload and discover resource consumption phases. We advance the existing technology to an incremental phase discovery method that appliesdoi:10.1109/cloud49709.2020.00070 fatcat:nylyu5iz3nhsvpk6rv7y25wype