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Resource-optimal scheduling for multi-graph workloads using supervisory control
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DOI：10.1016/j.jpdc.2026.105287.png)
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Abstract
En 中文
The escalating computational demands of modern real-time systems necessitate the deployment of complex tionalities on multi-core platforms. In these environments, applications are increasingly modeled as Directed Acyclic Task Graphs (DTGs) to capture intricate execution dependencies and inherent parallelism. While existing heuristic-based scheduling schemes provide scalable solutions, they often yield sub-optimal results by satisfying only sufficient schedulability conditions. To address this efficiency gap, this work proposes an optimal, off-line scheduler synthesis strategy for multiple recurrent DTGs using the Supervisory Control of Discrete Event Systems (SCDES) framework. By modeling task execution, resource availability, and stringent timing constraints within formal correct-by-construction paradigm, we provide a methodology that identifies optimal schedules where traditional heuristics fail. Comparative performance evaluations demonstrate that our synthesized schedulers can successfully manage a workload approximately similar to 20% greater than current state-of-the-art heuristic methods, significantly improving resource utilization and system acceptance rates in hardware-constrained environments.
Keywords:
Real-time systems
Task graphs
Multi-cores
Scheduling
Supervisory control
Discrete event systems
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4
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3.8K
Citations:
4.8K
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