{"ID":2921680,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T05:56:00.181519634Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01162","arxiv_id":"2606.01162","title":"Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts","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 of experts, each trained to manage different levels of deadline tightness. To our knowledge, DEFT is the first to introduce and validate a Mixture-of-Experts architecture for dynamic cloud workflow scheduling. By adaptively routing decisions through the most appropriate experts, DEFT is capable of meeting a broad spectrum of deadline requirements that no single expert can achieve. Central to DEFT is a \\textbf{graph-adaptive} gating mechanism that encodes workflow deadlines and DAGs, task states, and VM conditions, using cross-attention to guide expert activation in a fine-grained, deadline-sensitive manner. Experiments on dynamic cloud workflow benchmarks demonstrate that DEFT significantly reduces execution cost and deadline violations, outperforming multiple state-of-the-art DRL baselines.","short_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 t...","url_abs":"https://arxiv.org/abs/2606.01162","url_pdf":"https://arxiv.org/pdf/2606.01162v1","authors":"[\"Ya Shen\",\"Gang Chen\",\"Hui Ma\",\"Mengjie Zhang\"]","published":"2026-05-31T11:10:37Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Mixture of Experts\",\"Reinforcement Learning\"]","has_code":false}