arXiv:2605.23255v1 Announce Type: new Abstract: Learning-augmented algorithms have emerged as a powerful paradigm to surpass traditional worst-case lower bounds by integrating potentially noisy predictions. While this framework has seen success in online scheduling, existing work primarily optimizes job latency while relying on frequent, ``blind'' preemptions. This ignores the fundamental trade-off between algorithmic performance and preemption complexity. We provide the first systematic study of learning-augmented scheduling that curbs preemption while optimizing latency. We establish that th
The paper addresses a critical trade-off in learning-augmented scheduling as AI systems become more ubiquitous and their efficient operation becomes paramount, moving beyond theoretical advancements to practical resource management.
This research provides a foundational step towards more efficient and reliable AI systems by optimizing resource allocation with fewer disruptions, directly impacting the operational costs and performance of large-scale AI deployments.
Current AI scheduling paradigms, which often rely on frequent preemptions, could evolve to more parsimonious approaches, leading to greater system stability and potentially lower computational overheads.
- · AI infrastructure providers
- · Cloud computing platforms
- · Developers of real-time AI applications
- · Enterprises with large AI workloads
- · Inefficient AI scheduling algorithms
- · Systems relying on 'blind' preemption
More stable and resource-efficient AI agent operations due to improved scheduling.
Reduced operational costs and increased scalability for large AI models and agentic systems.
Accelerated development and deployment of complex AI agents that require robust and continuous performance without significant interruptions.
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Read at arXiv cs.LG