5 citations · 8 across the 4 of their papers we have counts for
5 papers
Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis
Xiaoyu Chu, Daniel Hofstätter, Shashikant Ilager +6
HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science,…
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN
Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e.g., mobile devices, IoT edge nodes). It…
CloudSim Express: A Novel Framework for Rapid Low Code Simulation of Cloud Computing Environments
Tharindu B. Hewage, Shashikant Ilager, Maria A. Rodriguez +1
Cloud computing environment simulators enable cost-effective experimentation of novel infrastructure designs and management approaches by avoiding significant costs incurred from r…
SymED: Adaptive and Online Symbolic Representation of Data on the Edge
Daniel Hofstätter, Shashikant Ilager, Ivan Lujic +1
The edge computing paradigm helps handle the Internet of Things (IoT) generated data in proximity to its source. Challenges occur in transferring, storing, and processing this rapi…
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
Alessandro Tundo, Marco Mobilio, Shashikant Ilager +3
The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resourc…