Serverless Implementations of Real-time Embarrassingly Parallel Problems

Abstract

In this paper, we conduct experiments to deploy a scalable serverless computing solution for real-time monitoring of thousands of patients with streaming electrocardiograms as an example of embarrassingly parallel tasks originally executed on two virtual machines. The research question is to find the speedup of such solution versus classical virtual machine approaches with sequential or parallel threads.The challenge of migrating an existing service to a serverless solution is to adapt and reconfigure the code for serverless platform, to write the code to invoke the service in parallel and asynchronously, and to use other services in the cloud that are needed for the whole solution to be functional and scalable. Evaluation of developing various solutions matching migration challenges to Google Cloud Run, Google Cloud Compute Engine, and Google Cloud Storage (customization of code, the configuration of services) shows that greater speedups can be achieved by dividing the Embarrassingly Parallel tasks into sub-tasks executed as a serverless service. We achieved highest speedup of almost 40 for Serverless solution compared to a sequential execution on a virtual machine solution, and speedup of 23 for Serverless solution compared to a Parallel execution using virtual machines.