Title: ChronosCore: Context-Aware Scheduling via Slack-Driven Temporal Reasoning
Primary area: reinforcement learning
Keywords: Reinforcement Learning, Real-Time Systems, Transformer Models, Attention Mechanisms, Scheduling under Uncertainty, Resource Allocation, Embedded AI

Abstract: Modern real-time systems demand schedulers that can autonomously manage complex temporal interactions without manual tuning. ChronosCore is a value-based agent that integrates a compact Transformer encoder into a Deep Q-Network. Temporal slack is discretized into learnable slack tokens and fed to an attention module that models global inter-task relations in parallel while remaining computationally lightweight. Extensive experiments on single-core benchmarks, industrial mixed-criticality traces, and large-scale multiprocessor workloads show that ChronosCore consistently surpasses classical fixed-priority algorithms and feedforward reinforcement learning baselines in deadline adherence and response time, while preserving sub-millisecond inference latency. Ablation studies on encoder depth, head count, and embedding size indicate that modest architectures attain the best trade-off between decision quality and runtime cost. Finally, attention visualizations reveal that high-attention regions align with critical deadline interactions, improving model transparency and aiding post-hoc analysis.

TL;DR: ChronosCore learns context-aware scheduling policies using slack-driven reasoning, improving deadline adherence and scalability in real-time systems.