Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts

arXiv:2606.01162v1 Announce Type: new Abstract: Workflow scheduling in cloud computing demands the intelligent allocation of dynamically arriving, graph-structured workflows with varying deadlines onto ever-changing virtual machine resources. However, existing deep reinforcement learning (DRL) schedulers remain limited by rigid, single-path inference architectures that struggle to handle diverse scheduling scenarios. We introduce \textbf{DEFT} (\textbf{D}eadline-p\textbf{E}rceptive Mixture-o\textbf{F}-Exper\textbf{t}s), an innovative DRL policy architecture that leverages a specialized mixture

Source: arXiv cs.AI — read the full report at the original publisher.

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