Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge

This repo provides instructions for extracting workload information from AI-Driven Animal Ecology (ADAE) studies. We also provide instructions for modelling ADAE studies as time-varying Poisson arrival rates. These simulated workloads can be used to test different scaling techniques (independent and correlated) and validate edge computing systems for ADAE studies in the field.

Acknowledgements

This work was funded by the National Science Foundation (NSF) grant OAC-2112006 (ICICLE AI Institute).