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

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