Solving a real-time allocation problem with constraint programming

Hladik, Pierre-Emmanuel; Cambazard, Hadrien; Déplanche, Anne-Marie; Jussien, Narendra

doi:10.1016/j.jss.2007.02.032

Cited by 48 publications

(25 citation statements)

References 41 publications

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“…Among those, several approaches target task real-time constraints such as task deadlines [Hladik et al 2008], or timeliness [Malapert et al 2012]. Preemptive scheduling problems have also been solved both with pure CP [Cambazard et al 2004], and with hybrid approaches combining GA with complete search [Yun and Gen 2002].…”

Section: Related Workmentioning

confidence: 99%

Combining Genetic Algorithms and Constraint Programming to Support Stress Testing of Task Deadlines

Alesio

Briand

Nejati

et al. 2015

ACM Trans. Softw. Eng. Methodol.

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Tasks in Real Time Embedded Systems (RTES) are often subject to hard deadlines, that constrain how quickly the system must react to external inputs. These inputs and their timing vary in a large domain depending on the environment state, and can never be fully predicted prior to system execution. Therefore, approaches for stress testing must be developed to uncover possible deadline misses of tasks for different input arrival times. In this paper, we describe stress test case generation as a search problem over the space of task arrival times. Specifically, we search for worst case scenarios maximizing deadline misses where each scenario characterizes a test case. In order to scale our search to large industrial-size problems, we combine two state-of-the-art search strategies, namely Genetic Algorithms (GA) and Constraint Programming (CP). Our experimental results show that, in comparison with GA and CP in isolation, GA+CP achieves nearly the same effectiveness as CP and the same efficiency and solution diversity as GA, thus combining the advantages of the two strategies. In light of these results, we conclude that a combined GA+CP approach to stress testing is more likely to scale to large and complex systems.

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Section: Related Workmentioning

confidence: 99%

Combining Genetic Algorithms and Constraint Programming to Support Stress Testing of Task Deadlines

Alesio

Briand

Nejati

et al. 2015

ACM Trans. Softw. Eng. Methodol.

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show abstract

“…Also, our task model is slightly less restrictive. Hladik et al (2008) handle the mapping of tasks to processors. As they use preemptive fixed-priority scheduling and do not support precedences, deciding schedulability is relatively simple.…”

Section: Off-line Mappingmentioning

confidence: 99%

Off-line mapping of multi-rate dependent task sets to many-core platforms

2015

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This paper presents an approach to execute safety-critical applications on multi-and many-core processors in a predictable manner. We investigate three concrete platforms: the Intel Single-chip Cloud Computer, the Texas Instruments TMS320C6678 and the Tilera TILEmpower-Gx36. We define an execution model to safely execute dependent periodic task sets on these platforms. The four rules of the execution model entail that an off-line mapping of the application to the platform must be computed. The paper details our approach to automatically compute a valid mapping. Furthermore, we evaluate our approach, which is based on constraint programming, by applying it to several task sets that are derived from industrial applications.

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“…Hladik. A.M. Deplanche, N. Jussien, H. Cambazard [11] has presented an approach to solve hard real time allocation problem i.e. to assign periodic tasks to processors in context of fixed priority preemptive scheduling.…”

Section: Review Analysismentioning

confidence: 99%

A Review of Constraint Programming

Dabas¹,

Cooner²

2014

IJCATR

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A constraint is defined as a logical relation among several unknown quantities or variables, each taking a value in a given domain. Constraint Programming (CP) is an emergent field in operations research. Constraint programming is based on feasibility which means finding a feasible solution rather than optimization which means finding an optimal solution and focuses on the constraints and variables domain rather than the objective functions. While defining a set of constraints, this may seem a simple way to model a real-world problem but finding a good model that works well with a chosen solver is not that easy. A model could be very hard to solve if it is poorly chosen.

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Solving a real-time allocation problem with constraint programming

Cited by 48 publications

References 41 publications

Combining Genetic Algorithms and Constraint Programming to Support Stress Testing of Task Deadlines

Combining Genetic Algorithms and Constraint Programming to Support Stress Testing of Task Deadlines

Off-line mapping of multi-rate dependent task sets to many-core platforms

A Review of Constraint Programming

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