activity
20182026
most citedDynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks

248 citations · 261 across the 23 of their papers we have counts for

collaborators
Showing cs.DCShow all

19 papers · 1 filter

cs.DC2026

A Technique for Load Shifting Low-latency Applications in Multi-Region Renewables Harvesting via SMT Core Pooling

Tharindu B. Hewage, Shashikant Ilager, Maria A. Rodriguez +1

Load shifting across geographic regions to chase intermittent renewable energy availability is commonly used in reducing cloud infrastructure carbon footprint. However, it often om…

cs.DC2026

Carbon-aware Resource Management for Latency-Sensitive Cloud Computing Environments: A Taxonomy and Future Directions

Tharindu B. Hewage, Shashikant Ilager, Maria Rodriguez Read +1

Proliferation of cloud-based latency-sensitive workloads requires infrastructures tuned to their workload-specific latency constraints. Today, they shape the cloud from a generaliz…

cs.DC2026

EcoKube: Simulating Carbon-Aware Scheduling Policies in Heterogeneous Edge-Cloud Environments

Gonçalo Ferreira, Shashikant Ilager

Energy demand from cloud and edge computing is rising rapidly, with AI workloads further intensifying electricity use and associated carbon emissions. In hybrid edge-cloud settings…

cs.DC2025

Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems

Tomasz Szydlo, Viacheslav Horbanov, Devki Nandan Jha +3

Edge computing has emerged as a pivotal technology, offering significant advantages such as low latency, enhanced data security, and reduced reliance on centralized cloud infrastru…

cs.DC2025

Aging-aware CPU Core Management for Embodied Carbon Amortization in Cloud LLM Inference

Tharindu B. Hewage, Shashikant Ilager, Maria Rodriguez Read +1

Broad adoption of Large Language Models (LLM) demands rapid expansions of cloud LLM inference clusters, leading to accumulation of embodied carbonthe emissions from manufacturin…

cs.DC20251 cited

GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code Generation

Shashikant Ilager, Lukas Florian Briem, Ivona Brandic

Large Language Models (LLMs) are becoming integral to daily life, showcasing their vast potential across various Natural Language Processing (NLP) tasks. Beyond NLP, LLMs are incre…