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- Agrawal, Kunal, et al.
(author)
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Optimal Scheduling of Measurement-Based Parallel Real-Time Tasks
- 2020
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In: Real-time systems. - : Springer Nature. - 0922-6443 .- 1573-1383. ; 56:3, s. 247-253
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Journal article (peer-reviewed)abstract
- In this work we consider a measurement-based model for parallel real-time tasks represented by the work and span parameters of directed acyclic graphs, with different bounds for nominal and overload scenarios. We address the corresponding real-time scheduling problem and propose an optimal scheduling strategy with a derived tight bound on the maximum response time of a task.
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| 2. |
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| 3. |
- Papadopoulos, Alessandro, Professor, et al.
(author)
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Feedback-based resource management for multi-threaded applications
- 2023
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In: Real-time systems. - : SPRINGER. - 0922-6443 .- 1573-1383.
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Journal article (peer-reviewed)abstract
- Reconciling the constraint of guaranteeing to always meet deadlines with the optimization objective of reducing waste of computing capacity lies at the heart of a large body of research on real-time systems. Most approaches to doing so require the application designer to specify a deeper characterization of the workload (and perhaps extensive profiling of its run-time behavior), which then enables shaping the resource assignment to the application. In practice, such approaches are weak as they load the designer with the heavy duty of a detailed workload characterization. We seek approaches for reducing the waste of computing resources for recurrent real-time workloads in the absence of such additional characterization, by monitoring the minimal information that needs to be observable about the run-time behavior of a real-time system: its response time. We propose two resource control strategies to assign resources: one based on binary-exponential search and the other, on principles of control. Both approaches are compared against the clairvoyant scenario in which the average/typical behavior is known. Via an extensive simulation, we show that both techniques are useful approaches to reducing resource computation while meeting hard deadlines.
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