arXiv:2603.23249v2 Announce Type: replace-cross Abstract: Efficient scheduling of directed acyclic graphs (DAGs) is a core problem in large-scale data-intensive computing systems, where query plans, data-processing workloads, and computation graphs consist of dependent tasks competing for limited heterogeneous resource pools. In practice, achieving high-performance execution requires schedulers to adapt across environments with varying resource pools and task types, while generating schedules under tight runtime budgets. We propose WeCAN, an end-to-end reinforcement learning framework for hete
The increasing complexity of AI workloads and the demand for efficient resource utilization in large-scale data systems necessitate advanced scheduling solutions.
Efficient scheduling of heterogeneous computing resources is crucial for optimizing performance and cost in AI and data-intensive applications, directly impacting large technology companies and specialized compute providers.
This research introduces an end-to-end reinforcement learning framework, 'WeCAN', aiming to significantly improve the adaptive scheduling of complex computational graphs across diverse hardware environments.
- · Cloud infrastructure providers
- · Large language model developers
- · Companies with data-intensive workloads
- · Specialized hardware manufacturers
- · Legacy scheduling software vendors
More efficient utilization of existing compute infrastructure for AI and data processing.
Reduced operational costs for large-scale AI training and inference, potentially enabling new, more complex models.
Enhanced accessibility to advanced computing for smaller players, fostering broader innovation in AI applications.
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Read at arXiv cs.AI