collaborators

11 papers

cs.DC2026

PLAIground: SLO-Driven Runtime Model Selection for Compound AI Systems in the Edge-Cloud-Space Continuum

Milos Gravara, Cynthia Marcelino, Andrija Stanisic +1

Applications in the 3D Computing Continuum, which unifies edge, cloud, and space, require combining multiple AI tasks such as object detection, time-series analytics, and natural l…

cs.DC2026

Design Methodology and Performance Trade-offs Management for Distributed and Compound AI Systems

Milos Gravara, Andrija Stanisic, Stefan Nastic

Artificial Intelligence (AI) systems must typically satisfy service-level objectives including accuracy, latency, and cost. The prevailing model-centric approaches select a monolit…

cs.DC2026

Compass: Optimizing Compound AI Workflows for Dynamic Adaptation

Milos Gravara, Juan Luis Herrera, Stefan Nastic

Compound AI is a distributed intelligence approach that represents a unified system orchestrating specialized AI/ML models with engineered software components into AI workflows. Co…

cs.DC2025

Gaia: Hybrid Hardware Acceleration for Serverless AI in the 3D Compute Continuum

Maximilian Reisecker, Cynthia Marcelino, Thomas Pusztai +1

Serverless computing offers elastic scaling and pay-per-use execution, making it well-suited for AI workloads. As these workloads run in heterogeneous environments such as the Edge…

cs.DC2025

Roadrunner: Accelerating Data Delivery to WebAssembly-Based Serverless Functions

Cynthia Marcelino, Thomas Pusztai, Stefan Nastic

Serverless computing provides infrastructure management and elastic auto-scaling, therefore reducing operational overhead. By design serverless functions are stateless, which means…

cs.DC2025

Lumos: Performance Characterization of WebAssembly as a Serverless Runtime in the Edge-Cloud Continuum

Cynthia Marcelino, Noah Krennmair, Thomas Pusztai +1

WebAssembly has emerged as a lightweight and portable runtime to execute serverless functions, particularly in heterogeneous and resource-constrained environments such as the Edge…