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Title: The Hitchhiker's Guide to Using Machine Learning in System-level Resource Management.

Abstract: This talk will take you through a journey of best practices, things to avoid and unconventional approaches for integrating machine learning methods in computer system-level resource management of cloud and high performance computing environments. These environments suffer from low resource utilization, due to the significant difference between resources allocated to the users and those actually used in practice. While the use of machine learning can lead to improved resource management and efficiency, its production-level use comes with significant overheads, engineering effort and interpretability concerns. This talk will inspire you to think outside-the-box and lead you to an existential question of whether machine learning is even necessary to use in certain aspects of system-level resource management.

Affiliation: Thaleia Dimitra Doudali, Assistant Professor, IMDEA Software Institute, Madrid, Spain.

Bio:Thaleia Dimitra Doudali is an Assistant Research Professor at the IMDEA Software Institute in Madrid, Spain. She received her PhD from the Georgia Institute of Technology (Georgia Tech) in the United States, advised by Ada Gavrilovska. Prior to that she earned anundergraduate diploma in Electrical and Computer Engineering at the National Technical University of Athens in Greece. Thaleia’s research lies at the intersection of Systems and Machine Learning, where she explores novel methodologies, such as machine learning and computer vision, to improve system-level resource management of emerging hardware technologies. In 2021, Thaleia received the Juan de la Cierva post-doctoral fellowship. In 2020, Thaleia was selected to attend the prestigious Rising Stars in EECS academic workshop. Aside from research, Thaleia actively strives to improve the mental health awareness in academia and foster diversity and inclusion.