12 papers
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…
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…
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…
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…