Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

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Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

Abstract

For a wide range of scientific studies such as weather, wildlife, and traffic, the visual data generated by network cameras can be valuable. For some of these studies, the resource demands for data analysis may fluctuate significantly (e.g., seasonal or only rush hours). Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras The pay-per-use of cloud computing may be a preferred solution to analyze large amounts of data from these network cameras. There are few studies on how many video streams from network cameras can be analysed using cloud resources.

Introduction

Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras Globally deployed network cameras provide valuable information, such as traffic, wildlife, and weather, to understand the world. Some of these studies can range from seasonal effects to just a few hours per day in time. Pay-per-use cloud computing can be a preferred solution for the provision of computational and storage resources for analysis for these studies. These studies can store data for offline analysis or retrieve live data for online analysis. In both scenarios, sufficient resources need to be allocated to meet the desired performance requirements.

System Configuration

H/W System Configuration
Speed                   : 1.1 GHz
RAM                      : 256 MB(min)
Hard Disk              : 20 GB
Floppy Drive          : 1.44 MB
Key Board             : Standard Windows Keyboard
Mouse                  : Two or Three Button Mouse
Monitor                : SVGA
S/W System Configuration

Platform                     :  cloud computing

Operating system       : Windows Xp,7,
Server                       : WAMP/Apache
Working on                : Browser Like Firefox, IE

Conclusion

In this paper we present an automated resource allocation system for analyzing a large amount of streaming data. First, we find that the performance-cost ratio of instances with fewer cores is better. We then propose two resource allocation strategies: linear increment method and predictive method and predictive method.