Collage Inference: Using Coded Redundancy for Lowering Latency Variation in Distributed Image Classification Systems

MLaaS (ML-as-a-Service) offerings by cloud computing platforms are becoming increasingly popular. Hosting pre-trained machine learning models in the cloud enables elastic scalability as the demand grows. But providing low latency and reducing the latency variance is a key requirement. Variance is harder to control in a cloud deployment due to uncertain-ties in resource allocations across many virtual instances. We propose the collage inference technique, which uses a novel convolutional neural network model, collage-cnn, to provide low-cost redundancy. A collage-cnn model takes a collage image formed by combining multiple images and performs multi-image classification in one shot, albeit at slightly lower accuracy. We augment a collection of traditional single image classifier models with a single collage-cnn classifier, which acts as their low-cost redundant backup. Collage-cnn provides backup classification results if any single image classification requests experience a slowdown. Deploying the collage-cnn models in the cloud, we demonstrate that the 99th percentile tail latency of inference can be reduced by 1.2x to 2x compared to replication-based approaches while providing high accuracy. Variation in inference latency can be reduced by 1.8x to 15x.