Shashikant Ilager

Postdoc Researcher, Vienna University Of Technology


Room HE0214

Favoritenstrasse 11

Vienna, Austria, 1040

Hello World!

I am a Postdoctoral Researcher at the High Performance Computing (HPC) Group, Vienna University of Technology (TU Wien), Austria. I work at the intersection of distributed systems, energy efficiency, and machine learning. In my research, I study how to design, optimize, and manage large-scale computing systems, such as cloud and edge platforms, that can support the growing demand for data-intensive and AI applications while minimizing environmental and economic costs. I ground my work in the characterization of distributed systems and optimization using learning-centric approaches. Recently, I have been exploring the energy efficiency of distributed AI applications to evaluate and improve their performance and sustainability.

Previously, I obtained my Ph.D. in Computer Science and Engineering at the CLOUDS Lab, University of Melbourne, Australia.


Dec 7, 2023 Our paper A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature has recieved Best paper award @ ACM/IEEE UCC 23.
Oct 15, 2023 I will be working as a visiting researcher (Oct 2023- Dec 2023) at INRIA, STACK team, hosted by Daniel Balouek @IMT Atlantique, Nantes, France.
Sep 26, 2023 Our paper on A Self-adaptive Energy-aware Appraoch for Edge-AI Application Management has been accepted @ASE 23. Please find the paper here
May 15, 2023 I visited CLOUDS lab @UniMelb and DisNet lab @Monash Univeristy, Australia and presented our recent work on edge monitoring and symbolic representation of data.
Dec 8, 2022 I presented our work on “Decentralized Edge Monitroing” at UCC conference, Vancouver, Washington, USA. Please check out the paper here.
Sep 26, 2022 I attended IC2E 2022 conference, Pacific Grove, California, USA.
Oct 20, 2021 My PhD thesis awarded Outstanding PhD Thesis Award by IEEE Technical Committee on Cloud Computing (IEEE TCCLD). Citation reads as “For outstanding research on machine learning-based energy and thermal efficient management of Cloud Data Centres”. For more details, please browse here.
May 20, 2020 Our paper on data-driven GPU frequency scaling has recieved Best paper award @ ACM/IEEE CCGRID 20.