mlsanityData to 14 Oct 2026

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

arXiv 2606.01162 ↗First posted 31 May 2026cs.AIcited by 0

On the map: Mixture-of-experts → Efficient MoE training and inference systems

Abstract (arXiv)

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 DEFT (Deadline-pErceptive Mixture-oF-Experts), 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 graph-adaptive gating mechanism that encodes workflow 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Efficient MoE training and inference systems, one of 12 directions in Mixture-of-experts. Builds on 16 of its 28 references that are in the map. First posted on arXiv 31 May 2026.

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16 of 28 references.

References · 16

  1. 2017Graph Attention Networkscited by 8,923
  2. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  3. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
  4. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 897
  5. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 590
  6. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  7. 2024A Survey on Mixture of Experts in Large Language Modelscited by 237
  8. 2019Learning Improvement Heuristics for Solving Routing Problemscited by 164
  9. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 140
  10. 2020Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problemscited by 100
  11. 2018Evolution-Guided Policy Gradient in Reinforcement Learningcited by 83
  12. 2024MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Expertscited by 70
4 more
  1. 2021Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directionscited by 14
  2. 2025SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchycited by 8
  3. 2024Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learningcited by 4
  4. 2025GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategycited by 1

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  4. 2024An Advanced Reinforcement Learning Framework for Online Scheduling of Deferrable Workloads in Cloud Computing
  5. 2024Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud
  6. 2026HiGFRL: Hierarchical Graph Fusion-Driven Reinforcement Learning for Dependency-Aware Task Scheduling in Heterogeneous Cloud
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