Deploying LLM-enabled agentic applications across the Edge-to-Cloud continuum remains challenging due to hardware heterogeneity, deployment complexity, limited observability, and the lack of systematic evaluation methods. Existing solutions address agent development, observability, or benchmarking separately, offering...
Michalis Kasioulis, Moysis Symeonides, G. Pallis et al.· 0 citations
MCP-GRANITE is presented, an open-source extensible benchmark framework that treats tool-interface granularity as a controlled variable for MCP-based agents, evaluated under edge and IoT scenarios and identifies tool-interface granularity as a key design parameter for MCP-based agents.
Demetris Paschalides, Moysis Symeonides, G. Pallis et al.· 0 citations
A controlled measurement study of self-hosted LLM inference across edge and near-edge deployment nodes: an NVIDIA Jetson AGX Orin and a near-edge server with CPU-only and GPU-enabled inference modes, highlighting that compute-side inference metrics alone can lead to suboptimal placement for latency-sensitive interactiv...
Maysam Khatib, Moysis Symeonides, Demetris Trihinas et al.· 0 citations
InFactPlanner presents InFactPlanner, a trace-driven decision-support framework for what-if analysis of sustainable AI data center deployment for LLM inference across single and geo-distributed sites, and demonstrates scenario-driven decision analyses for hardware selection, renewable placement, geographic deployment,...
Nicoletta Tsiopani, Moysis Symeonides, G. Pallis et al.· 0 citations
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