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
The accelerating complexity and scale of AI models and cloud-native applications necessitate more sophisticated scheduling to meet performance and cost targets as cloud compute demand skyrockets.
Efficient cloud workflow scheduling directly impacts the cost, latency, and scalability of AI and other data-intensive operations, making advanced techniques critical for leveraging compute resources effectively.
Traditional rigid scheduling architectures are being replaced by adaptive, AI-driven methods that can dynamically optimize resource allocation for diverse and time-sensitive workloads.
- · Cloud providers
- · AI developers
- · Deep reinforcement learning researchers
- · Companies with highly dynamic compute needs
- · Legacy scheduling software vendors
- · Organizations using static resource allocation
- · Applications with bursty or unpredictable demand
Improved efficiency and reduced operational costs for cloud computing users running complex workflows.
Enables the development and deployment of more complex, real-time AI agents and systems that rely on agile cloud infrastructure.
Could indirectly accelerate the capabilities and ubiquity of AI agents by making their underlying compute more accessible and performant.
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Read at arXiv cs.AI