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.

Prof. Radu Prodan

SOFA 2020: Guest of Honor and Talk

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Prof. Radu Prodan ist guest of honor at the 9th International Workshop on Soft Computing Applications (SOFA), 27-29 Nov 2020, Arad, Romania. The title of his talk is “Distribute one Billion”.

Prof. Radu Prodan

Project ADAPT: Newspaper Article in “Kronen Zeitung”

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The newspaper “Kronen Zeitung” published the article “IM KAMPF GEGEN CORONA: Universität Klagenfurt forscht mit den Chinesen” with Prof. Radu Prodan.

Cluster Computing paper: FastTTPS: Fast Approach for Video Transcoding Time Prediction and Scheduling for HTTP Adaptive Streaming Videos

Authors: Prateek Agrawal (University of Klagenfurt, Austria), Anatoliy Zabrovskiy (University of Klagenfurt, Austria), Adithyan Ilagovan (Bitmovin Inc., CA, USA), Christian Timmerer (University of Klagenfurt, Austria), Radu Prodan (University of Klagenfurt, Austria)

Abstract: HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called Fast video Transcoding Time Prediction and Scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. Read more

Prof. Radu Prodan

Project ADAPT: newspaper article in “Kleine Zeitung”

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The newspaper “Kleine Zeitung” published the article “Medizinische Schutzausrüstung: Neue IT-Lösung soll Menschenleben retten” with Prof. Radu Prodan.