Cocktail: A Multidimensional Optimization for Model Serving in Cloud

With a growing demand for adopting ML models for a variety of application services, it is vital that the frameworks serving these models are capable of delivering highly accurate predictions with minimal latency along with reduced deployment costs in a public cloud environment. Despite high latency, prior works in this domain are crucially limited by the accuracy offered by individual models. Intuitively, model ensembling can address the accuracy gap by intelligently combining different models in parallel. However, selecting the appropriate models dynamically at runtime to meet the desired accuracy with low latency at minimal deployment cost is a nontrivial problem. Towards this, we propose Cocktail, a cost effective ensembling-based model serving framework. Cocktail comprises of two key components: (i) a dynamic model selection framework, which reduces the number of models in the ensemble, while satisfying the accuracy and latency requirements; (ii) an adaptive resource management (RM) framework that employs a distributed proactive autoscaling policy combined with importance sampling, to efficiently allocate resources for the models. The RM framework leverages transient virtual machine (VM) instances to reduce the deployment cost in a public cloud. A prototype implementation of Cocktail on the AWS EC2 platform and exhaustive evaluations using a variety of workloads demonstrate that {Cocktail} can reduce deployment cost by 1.45x, while providing 2x reduction in latency and satisfying the target accuracy for up to 96% of the requests, when compared to state-of-the-art model-serving frameworks.