A programming model and middleware for high throughput serverless computing applications

Abstract

Serverless computing has introduced unprecedented levels of scalability and parallelism for the execution of High Throughput Computing tasks. This represents a challenge and an opportunity for different scientific workloads to be adapted to upcoming programming models that simplify the usage of such platforms. In this paper we introduce a serverless model for highly-parallel file-processing applications. We also describe a middleware implementation that supports the execution of customized execution environments based on Docker images on AWS Lambda, the leading serverless computing platform. Moreover, this middleware offers tools to manage the input/output of the serverless infrastructure and the creation of HTTP endpoints in a transparent way to the user. To test the programming model proposed and the middleware, this paper describes two case studies. The first one analyzes medical images with a high degree of parallelism. The second one presents an architecture to process video keyframes. The results from both case studies are discussed and a cost analysis of the medical image architecture comparing different Cloud options is carried out. The results show that the combination of a high-level programming model with the scalable capabilities of AWS Lambda makes it easy for end users to efficiently exploit serverless computing for the optimized and cost-effective execution of loosely-coupled tasks.