YAIFS turns fog simulations interactive via layered APIs and MCP so heterogeneous agents including LLMs can monitor and adapt placements at runtime.
Multi-objective application placement in fog computing using graph neural network-based reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.DC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
AGMARL-DKS uses per-node multi-agent RL with GNN state representations and stress-aware lexicographical ordering to outperform the default Kubernetes scheduler on fault tolerance, utilization, and cost for batch and mission-critical workloads.
citing papers explorer
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YAIFS: Yet (not) Another Intelligent Fog Simulator: A Framework for Agent-Driven Computing Continuum Modeling & Simulation
YAIFS turns fog simulations interactive via layered APIs and MCP so heterogeneous agents including LLMs can monitor and adapt placements at runtime.
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AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
AGMARL-DKS uses per-node multi-agent RL with GNN state representations and stress-aware lexicographical ordering to outperform the default Kubernetes scheduler on fault tolerance, utilization, and cost for batch and mission-critical workloads.