| Title: | Distributed Feature Extraction Using Cloud Computing Resources |
| Author: | Dalton, Steven |
| Abstract: | The need to expand the computational resources in a massive surveillance network is clear but traditional means of purchasing new equipment for short-term tasks every year is wasteful. In this work I will provide evidence in support of utilizing a cloud computing infrastructure to perform computationally intensive feature extraction tasks on data streams. Efficient off-loading of computational tasks to cloud resources will require a minimization of the time needed to expand the cloud resources, an efficient model of communication and a study of the interplay between the in-network computational resources and remote resources in the cloud. This report provides strong evidence that the use of cloud computing resources in a near real-time distributed sensor network surveillance system, ASAP, is feasible. A face detection web service operating on an Amazon EC2 instance is shown to provide processing of 10-15 frames per second. |
| Type: | Undergraduate Thesis |
| URI: | http://hdl.handle.net/1853/28138 |
| Date: | 2009-05-04 |
| Publisher: | Georgia Institute of Technology |
| Subject: |
Utility computing
Cloud computing Distributed computing |
| Department: | Computer Science |
| Advisor: | Umakishore Ramachandran - Faculty Mentor ; Rajnish Kumar - Committee Member/Second Reader |
| Files | Size | Format | View |
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| dalton_steven_t_200905_ro.pdf | 342.7Kb |
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