EP-NCO uses dual-graph GNN embeddings and RL policies to reduce service response time by 46-50% versus metaheuristics in simulated edge-cloud placements.
ACM Computing Surveys53(3) (2020) https://doi
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A GNN-based DRL model with two actor-critics produces comparable Pareto fronts for multi-objective fog application placement in milliseconds versus hours for genetic algorithms.
citing papers explorer
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Latency-Aware Service Placement using Neural Combinatorial Optimisers for Edge--Cloud Systems
EP-NCO uses dual-graph GNN embeddings and RL policies to reduce service response time by 46-50% versus metaheuristics in simulated edge-cloud placements.
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Multi-objective application placement in fog computing using graph neural network-based reinforcement learning
A GNN-based DRL model with two actor-critics produces comparable Pareto fronts for multi-objective fog application placement in milliseconds versus hours for genetic algorithms.