Partitioned Scheduling for Dependency Graphs in Multiprocessor Real-Time Systems

Shi, Junjie; Ueter, Niklas; Brüggen, Georg von der; Chen, Jian-Jia

doi:10.1109/rtcsa.2019.8864591

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…We name these algorithms by combining: 1) JS/PRP: the two different dependency graph generation methods. 2) LEDF/PEDF: to schedule the generated graph, we used the LIST-EDF in [36] (LEDF) or partitioned EDF (PEDF) in [37], and a worst-fit partitioning algorithm. 3) P/NP: preemptive or non-preemptive schedule for critical sections.…”

Section: Evaluations Setupmentioning

confidence: 99%

“…Specifically, for semi-partitioned scheduling, the LIST-EDF in [36] based on classical list scheduling by Graham [19] can be applied, i.e., whenever a processor idles and at least one sub-job is eligible, the sub-job with the earliest deadline starts its execution on the processor. Additionally, its partitioned extension in [37] (P-EDF) can be applied to generate the partitioned schedule. (a) A dependency graph of a task set with two binary semaphores.…”

Section: The Dga Based On Job/flow Shopmentioning

confidence: 99%

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Scheduling of Real-Time Tasks With Multiple Critical Sections in Multiprocessor Systems

Chen

Shi

Brüggen

et al. 2022

IEEE Trans. Comput.

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The performance of multiprocessor synchronization and locking protocols is a key factor to utilize the computation power of multiprocessor systems under real-time constraints. While multiple protocols have been developed in the past decades, their performance highly depends on the task partition and prioritization. The recently proposed Dependency Graph Approach showed its advantages and attracted a lot of interest. It is, however, restricted to task sets where each task has at most one critical section. In this paper, we remove this restriction and demonstrate how to utilize algorithms for the classical job shop scheduling problem to construct a dependency graph for tasks with multiple critical sections. To show the applicability, we discuss the implementation in LITMUS RT and report the overheads. Moreover, we provide extensive numerical evaluations under different configurations, which in many situations show significant improvement compared to the state-of-the-art.

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Section: Evaluations Setupmentioning

confidence: 99%

Section: The Dga Based On Job/flow Shopmentioning

confidence: 99%

Scheduling of Real-Time Tasks With Multiple Critical Sections in Multiprocessor Systems

Chen

Shi

Brüggen

et al. 2022

IEEE Trans. Comput.

Self Cite

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“…Here, the goal is to improve the parallel execution efficiency of tasks that share resources (e.g., external devices, shared memory, and files). To solve this problem, there exist multiple paradigms, such as a Partitioned Schedule [43], [45], a Global Schedule [46], and a Semi-partitioned Schedule [44].…”

Section: A Balanced Partition (Bp)mentioning

confidence: 99%

Sequence-Based Selection Hyper-Heuristic Model via MAP-Elites

et al. 2021

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Combinatorial optimization problems (COPs) are paramount for real-life problems with discrete variables. Even though the number of combinations is finite, some problems exhibit exponential growth, rendering exact approaches unfeasible. So, approximate methods, such as heuristics, are customary for making fast decisions. Despite their small computational cost, each heuristic specializes in specific kinds of problems. Hence, other approaches have appeared for merging their strengths. One of these approaches is commonly known as selection hyper-heuristics. However, a trained hyper-heuristic usually carries the following problem: it provides scarce information about its sensitivity. This makes it more difficult to estimate the effect of changing a parameter. Illumination algorithms seek to improve this issue by focusing on exploration rather than on exploitation while preserving information about the best solutions with different criteria. Still, literature falls short when merging both approaches, representing a knowledge gap. Therefore, in this work, we test the feasibility of using an illumination algorithm, the MAP-Elites (ME), for tuning a sequence-based selection hyper-heuristic model for Balanced Partition problems. We choose ME since it has been successfully applied to a different COP, and by linking it with hyper-heuristics, we may exploit upon its benefits. So, we may achieve a hyper-heuristic that represents the best combination of heuristics. Simultaneously, we can gain information about the expected performance of diverse hyperheuristics, e.g., those that begin with a different heuristic. Our approach operates by creating a multidimensional map, where each design variable represents the application of a given heuristic. Afterward, ME generates mutated sequences and tests them to determine if they represent a better-performing solution. To test our approach, we consider 1500 instances that include easy and hard instances, analyzed under different scenarios. We also include limit instances that are neither easy nor hard. Our resulting data supports the feasibility of the proposed approach. We show that this kind of hyper-heuristic can perform toe-to-toe with a synthetic oracle. Besides, under some conditions, the former may even outperform the latter. This represents an outstanding result, since such an oracle can only be obtained by a brute-force approach, and because it demonstrates that merging both approaches (ME and hyper-heuristics) is a path worth pursuing. We also present how each parameter affects the model performance, concluding that some parameters can be critical while others are virtually irrelevant. This is also a relevant result, since it serves as the groundwork for future works so that they can focus on exploiting the most relevant parameters.

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