GPU-Enabled Serverless Workflows for Efficient Multimedia Processing

GPU-Enabled Serverless Workflows for Efficient Multimedia Processing

Blog Article

Serverless computing has introduced scalable event-driven processing in Cloud infrastructures.However, it is not trivial for multimedia processing to benefit from the elastic capabilities featured by serverless applications.To this aim, this paper introduces the evolution of a framework to support the execution of customized runtime environments in AWS Lambda in order to accommodate workloads that do not satisfy its strict computational requirements: increased execution times and the ability to use GPU-based resources.This has been D-Ribose achieved through the integration of AWS Batch, a managed service to deploy virtual elastic clusters for the execution of containerized jobs.

In addition, a Functions Definition Language (FDL) is introduced for the description of data-driven workflows of Fascinator functions.These workflows can simultaneously leverage both AWS Lambda for the highly-scalable execution of short jobs and AWS Batch, for the execution of compute-intensive jobs that can profit from GPU-based computing.To assess the developed open-source framework, we executed a case study for efficient serverless video processing.The workflow automatically generates subtitles based on the audio and applies GPU-based object recognition to the video frames, thus simultaneously harnessing different computing services.

This allows for the creation of cost-effective highly-parallel scale-to-zero serverless workflows in AWS.