Paper accepted: Energy Consumption Analysis of R-based Machine Learning Algorithms for Pandemic Predictions


Authors: Shajulin Benedict (IIIT Kottayam, India), Prateek Agrawal (University of Klagenfurt, Austria & Lovely Professional University, India) , Radu Prodan (University of Klagenfurt, Austria)

Abstract: The push for agile pandemic analytic solutions has rapidly attained development-stage software modules instead of functioning as full-fledged production-stage products — i.e., performance, scalability, and energy-related concerns need to be optimized for the underlying computing domains. And while the research continues to support the idea that reducing the energy consumption of algorithms improves the lifetime of battery-operated machines, advisable tools in almost any developer setting, an energy analysis report for R-based analytic programs is indeed a valuable suggestion. This article proposes an energy analysis framework for R-programs that enables data analytic developers, including pandemic-related application developers, to analyze code. It reveals an energy analysis report for R programs written to predict the new cases of 215 countries using random forest variants. Experiments were carried out at the IoT cloud research lab and the energy efficiency aspects were discussed in the article. In the experiments, ranger-based prediction program consumed 95.8 Joules.

4th International Conference on Advanced Informatics for Computing Research (ICAICR-2020) 


Acknowledgement: This work is supported by IIIT-Kottayam faculty research fund and OEAD-DST fund.