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Resource-Aware Scheduling(RAS)
Dynamically schedules AI tasks based on available computational resources and constraints
In 30 seconds
- What
- Queues AI tasks and adjusts their priority and timing based on live CPU, GPU, memory, temperature, and power measurements.
- When to use
- Systems running multiple AI workloads with hard deadlines, limited compute, or thermal constraints that change during operation.
- Watch out
- Overhead from constant monitoring can exceed savings; priority thrashing under resource contention wastes more time than it reclaims.
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Resource-Aware Scheduling: Overview
Dynamically schedules AI tasks based on available computational resources and constraints
- Real-time resource monitoring
- Dynamic priority adjustment
- Multi-resource optimization
- Deadline-aware scheduling
- Energy consumption balancing
- Thermal management integration
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References
The papers, specifications, and repositories this pattern is based on.
- Clockwork: Predictable Low-Latency Inference (OSDI 2020)
- Nexus: Multi-tenant Inference Serving (SOSP 2019)
- INFaaS: Automated Model-less Inference Serving (ATC 2021)
- Salus: Fine-Grained GPU Sharing
- Gandiva: Introspective GPU Scheduler (OSDI 2018)
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