# Application of Google Cloud Platform in Astrophysics

## TL;DR

This paper explores the use of Google Cloud Platform for astrophysics research by implementing and evaluating nine proof-of-concept projects, demonstrating cloud-based scientific workflows and comparing costs and performance with traditional on-site facilities.

## Contribution

It presents nine proof-of-concept implementations of cloud-based astrophysics workflows, highlighting architecture, performance, and cost advantages of GCP for scientific computing.

## Key findings

- Successful deployment of scientific software as microservices on GCP
- Comparison of cloud and on-site performance and costs
- Implementation of HPC-based data reduction pipelines

## Abstract

The availability of new Cloud Platform offered by Google motivated us to propose nine Proof of Concepts (PoC) aiming to demonstrated and test the capabilities of the platform in the context of scientifically-driven tasks and requirements. We review the status of our initiative by illustrating 3 out of 9 successfully closed PoC that we implemented on Google Cloud Platform. In particular, we illustrate a cloud architecture for deployment of scientific software as microservice coupling Google Compute Engine with Docker and Pub/Sub to dispatch heavily parallel simulations. We detail also an experiment for HPC based simulation and workflow executions of data reduction pipelines (for the TNG-GIANO-B spectrograph) deployed on GCP. We compare and contrast our experience with on-site facilities comparing advantages and disadvantages both in terms of total cost of ownership and reached performances.