Real-Time Scheduling
Overview
Real-time scheduling decides when and where tasks run so control loops, data flows, and user requests finish within their deadlines. Common approaches include fixed-priority and earliest-deadline-first policies, DAG and workflow schedulers, resource-aware dispatching, and optimization or reinforcement learning; profiling and schedulability analysis help estimate whether deadlines can be met. Applications range from embedded and robotic systems to cloud workflows, industrial production, and machine-learning inference, where deadlines may compete with throughput, energy, accuracy, and fairness goals. A central challenge is maintaining predictable timing under changing workloads and shared-resource interference without making online decisions too costly or sacrificing efficiency.
Latest papers
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