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Tuning Drone Data Delivery and Analysis on the Public Cloud
Abstract
Currently, transfering a huge amount of video data from on premise to the public cloud is very slow. In this article, the researcher uses a set of self-developed software written in Python, C, and Bash to improve the speed of data transmission and analysis of drone-generated videos taken in eight different cities on the American continent to the public cloud of Amazon Web Services. The author uses several tools, compression, parallel threads execution, and the autoscaling feature of the public cloud vendor to tune the process of transmission and analysis in 78% of the speed compared to the common sequential transmission and single node option.
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