248 citations · 269 across the 27 of their papers we have counts for
7 papers · 1 filter
A Framework for Carbon-aware Real-Time Workload Management in Clouds using Renewables-driven Cores
Tharindu B. Hewage, Shashikant Ilager, Maria A. Rodriguez +1
Cloud platforms commonly exploit workload temporal flexibility to reduce their carbon emissions. They suspend/resume workload execution for when and where the energy is greenest. H…
ABBA-VSM: Time Series Classification using Symbolic Representation on the Edge
Meerzhan Kanatbekova, Shashikant Ilager, Ivona Brandic
In recent years, Edge AI has become more prevalent with applications across various industries, from environmental monitoring to smart city management. Edge AI facilitates the proc…
DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge
Daniel May, Alessandro Tundo, Shashikant Ilager +1
The deployment of ML models on edge devices is challenged by limited computational resources and energy availability. While split computing enables the decomposition of large neura…
FRESCO: Fast and Reliable Edge Offloading with Reputation-based Hybrid Smart Contracts
Josip Zilic, Vincenzo de Maio, Shashikant Ilager +1
Mobile devices offload latency-sensitive application tasks to edge servers to satisfy applications' Quality of Service (QoS) deadlines. Consequently, ensuring reliable offloading w…
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,…
A Decentralized and Self-Adaptive Approach for Monitoring Volatile Edge Environments
Shashikant Ilager, Jakob Fahringer, Alessandro Tundo +1
Edge computing provides resources for IoT workloads at the network edge. Monitoring systems are vital for efficiently managing resources and application workloads by collecting, st…
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…