248 citations · 269 across the 27 of their papers we have counts for
29 papers
DySCo: Dynamic Sharding for Collaborative Edge-Cloud LLM Inference with Depth-Synchronized Batching
Jingpo Xu, Paul Joe Maliakel, Ivona Brandic +1
Pervasive intelligent applications are increasingly deployed on mobile and Internet of Things (IoT) edge devices. Consequently, Large Language Models (LLMs) are increasingly used t…
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…
Carbon-aware Resource Management for Latency-Sensitive Cloud Computing Environments: A Taxonomy and Future Directions
Tharindu B. Hewage, Shashikant Ilager, Maria Rodriguez Read +1
The proliferation of cloud-based latency-sensitive workloads, such as autonomous driving, real-time healthcare services, industrial automation, immersive interactive services, and…
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…
Leveraging LLMs for Structured Information Extraction and Analysis from Cloud Incident Reports (Work In Progress Paper)
Xiaoyu Chu, Shashikant Ilager, Yizhen Zang +2
Incident management is essential to maintain the reliability and availability of cloud computing services. Cloud vendors typically disclose incident reports to the public, summariz…
GreenServ: Energy-Efficient Context-Aware Dynamic Routing for Multi-Model LLM Inference
Thomas Ziller, Shashikant Ilager, Alessandro Tundo +3
Large language models (LLMs) demonstrate remarkable capabilities, but their broad deployment is limited by significant computational resource demands, particularly energy consumpti…
ARKV: Adaptive and Resource-Efficient KV Cache Management under Limited Memory Budget for Long-Context Inference in LLMs
Jianlong Lei, Shashikant Ilager
Large Language Models (LLMs) are increasingly deployed in scenarios demanding ultra-long context reasoning, such as agentic workflows and deep research understanding. However, long…
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…
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…
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…
Characterizing LLM Inference Energy-Performance Tradeoffs across Workloads and GPU Scaling
Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
LLM inference exhibits substantial variability across queries and execution phases, yet inference configurations are often applied uniformly. We present a measurement-driven charac…
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…
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…
Energy-Efficiency and Sustainability in New Generation Cloud Computing: A Vision and Directions for Integrated Management of Data Centre Resources and Workloads
Rajkumar Buyya, Shashikant Ilager, Patricia Arroba
Cloud computing has become a critical infrastructure for modern society, like electric power grids and roads. As the backbone of the modern economy, it offers subscription-based co…
Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future Directions
Shashikant Ilager, Rajkumar Buyya
This paper investigates the existing resource management approaches in Cloud Data Centres for energy and thermal efficiency. It identifies the need for integrated computing and coo…
Thermal Prediction for Efficient Energy Management of Clouds using Machine Learning
Shashikant Ilager, Kotagiri Ramamohanarao, Rajkumar Buyya
Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects r…