# Choosing an effective setup for stream processing

## TL;DR

This study compares edge computing and public cloud for stream data processing in IoT manufacturing, finding edge computing more cost-effective and slightly better in latency and resource utilization.

## Contribution

It provides an empirical evaluation of edge versus cloud computing for IoT stream processing, highlighting cost and performance benefits of edge solutions.

## Key findings

- Edge computing achieves slightly higher throughput.
- Edge node uses less resources than cloud node.
- Edge setup is more cost-efficient with IaaS providers.

## Abstract

This project aims to study the feasibility and cost-effectiveness of using edge computing for stream data processing in the context of Internet of Things (IoT) in manufacturing in Europe. Two scenarios were considered: using edge computing to reduce latency and using a popular public cloud provider. Both scenarios demonstrated high throughput, with the edge computing scenario slightly outperforming the public cloud scenario. The impact on resource utilization was also measured, with the edge node showing slightly lower resource usage than the cloud node. The experiment concluded that running the system at the edge is more cost-efficient, but only using any Infrastructure as a Service (IaaS) provider acting as the infrastructure provider. IaaS providers will be crucial in offering edge solutions and identifying geographical areas where regional data centers could be used as points of presence for low-latency applications. Keywords: edge computing, stream data processing, Internet of Things (IoT), manufacturing, Europe, latency, throughput, resource utilization, cost-efficiency, infrastructure as a service (IaaS), regional data centers, low-latency applications, cloud computing, feasibility study.