arXiv:2501.12942v2 Announce Type: replace Abstract: Effective multi-user delay-constrained scheduling is crucial in various real-world applications, including embodied AI, instant messaging, live streaming, and data center management, where efficient resource allocation is required among users with diverse delay sensitivities. In these scenarios, schedulers must make real-time decisions to satisfy both delay and resource constraints without prior knowledge of system dynamics, which are often time-varying and challenging to estimate. {Current learning-based methods typically require online inte
The increasing complexity of real-world AI applications, embodied AI, and large-scale data systems necessitates more robust and dynamic scheduling solutions.
This development addresses a critical bottleneck in deploying autonomous and AI-driven systems by improving real-time resource allocation under dynamic, unknown conditions, enhancing efficiency and reliability.
The shift from traditional online learning to offline diffusion policies significantly enhances the ability of scheduling systems to adapt to time-varying dynamics without prior knowledge, making them more resilient and effective.
- · Embodied AI developers
- · Data center operators
- · Real-time communication platforms
- · AI agents
- · Legacy scheduling algorithm providers
- · Systems with static resource allocation
Improved performance and stability in applications requiring real-time, delay-constrained scheduling.
Accelerated development and wider deployment of complex multi-user AI systems and embodied AI due to more dependable resource management.
Potential for new business models and services built on highly efficient, autonomously managed distributed systems.
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Read at arXiv cs.AI