11 citations · 31 across the 15 of their papers we have counts for
6 papers · 1 filter
Atlas: Optimizing Deployment of Compound AI Workflows on Heterogeneous Clusters
Milos Gravara, Andrija Stanisic, Stefan Nastic
Compound AI workflows are increasingly used to serve complex AI tasks by coordinating multiple AI models and software components. This approach enables deployment flexibility, as e…
Constella: A Novel Framework for Cost-Efficient Distributed AI Inference in LEO Space Data Centers
Andrija Stanisic, Milos Gravara, Juan Luis Herrera +1
Space data centers built from Low-Earth Orbit (LEO) satellite constellations are gaining increasing attention as a scalable computing infrastructure. With access to abundant solar…
STORE: Self-Provisioning Storage-as-a-Service for Serverless Functions
Florian Trimmel, Cynthia Marcelino, Thomas Pusztai +1
Serverless computing provides on-demand elasticity, pay-per-use, and simplified deployment. However, serverless functions are typically stateless and depend on external storage ser…
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…
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…
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…