FaPES: Enabling Efficient Elastic Scaling for Serverless Machine Learning Platforms

Serverless computing platforms have become increasingly popular for running machine learning (ML) tasks due to their user-friendliness and decoupling from underlying infrastructure. However, auto-scaling to efficiently serve incoming requests still remains a challenge, especially for distributed ML training or inference jobs in a serverless GPU cluster. Distributed training and inference jobs are highly sensitive to resource configurations, and demand high model efficiency throughout their lifecycle. We propose FaPES, a FaaS-oriented Performance-aware Elastic Scaling system to enable efficient resource allocation in serverless platforms for ML jobs. FaPES enables flexible resource loaning between virtual clusters for running training and inference jobs. For running inference jobs, servers are reclaimed on demand with minimal preemption overhead to guarantee service level objective (SLO); for training jobs, optimal GPU allocation and model hyperparameters are jointly adapted based on an ML-based performance model and a resource usage prediction board, alleviating users from model tuning and resource specification. Evaluation on a 128-GPU testbed demonstrates up to 24.8% job completion time reduction and ×1.8 Goodput improvement, as compared to representative elastic scaling schemes.