# Cloud Resource Optimization for Processing Multiple Streams of Visual Data

## TL;DR

This paper presents a cloud resource management approach for analyzing real-time visual data streams from network cameras, achieving significant cost savings by optimizing instance types and allocations.

## Contribution

It introduces a novel resource allocation strategy that considers analysis types, stream counts, and camera locations to optimize cloud resource usage for real-time visual data processing.

## Key findings

- Over 50% cost reduction demonstrated on AWS
- Effective resource allocation for real-time camera data analysis
- Optimized selection of cloud instance types

## Abstract

Hundreds of millions of network cameras have been installed throughout the world. Each is capable of providing a vast amount of real-time data. Analyzing the massive data generated by these cameras requires significant computational resources and the demands may vary over time. Cloud computing shows the most promise to provide the needed resources on demand. In this article, we investigate how to allocate cloud resources when analyzing real-time data streams from network cameras. A resource manager considers many factors that affect its decisions, including the types of analysis, the number of data streams, and the locations of the cameras. The manager then selects the most cost-efficient types of cloud instances (e.g. CPU vs. GPGPU) to meet the computational demands for analyzing streams. We evaluate the effectiveness of our approach using Amazon Web Services. Experiments demonstrate more than 50% cost reduction for real workloads.