A systematic mapping study of 87 papers derives an architecture-based taxonomy for Workflow as a Service brokers and identifies future research directions.
IEEE Transactions on Emerging Topics in Computational Intelligence
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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.
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Workflow as a Service Broker in Cloud Environment: A Systematic Mapping Study
A systematic mapping study of 87 papers derives an architecture-based taxonomy for Workflow as a Service brokers and identifies future research directions.
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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.