A new method for optimization of allocation and scheduling in real time applications

Coli, M.; Palazzari, P.

doi:10.1109/emwrts.1995.514320

Cited by 15 publications

(8 citation statements)

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“…Since the problem of allocating tasks is generally NP-hard [34], some form of enumerative method or approximation using heuristics needs to be developed for this problem: graph theory techniques [57,38,18], branch-andbound [41,45,44,50,65], genetic algorithm [40,6,53,2,39,19,20,42,21], clustering [4,1,36], steepest descent (or Hill climbing) [40,64], tabu search [46,64]; simulated annealing [63,11,40,14,15,17,19,64], neural network [56,2], and dedicated heuristics [54,17,32,66,3]. However, today, for a specific problem, no technique seems to be more appropriate than another.…”

Section: Introductionmentioning

confidence: 99%

Solving a real-time allocation problem with constraint programming

Hladik

Cambazard

Déplanche

et al. 2008

Journal of Systems and Software

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In this paper, we present an original approach (CPRTA for "Constraint Programming for solving Real-Time Allocation") based on constraint programming to solve a static allocation problem of hard real-time tasks. This problem consists in assigning periodic tasks to distributed processors in the context of fixed priority preemptive scheduling. CPRTA is built on dynamic constraint programming together with a learning method to find a feasible processor allocation under constraints. Two efficient new approaches are proposed and validated with experimental results. Moreover, CPRTA exhibits very interesting properties. It is complete (if a problem has no solution, the algorithm is able to prove it); it is non-parametric (it does not require specific tuning) thus allowing a large diversity of models to be easily considered. Finally, thanks to its capacity to explain failures, it offers attractive perspectives for guiding the architectural design process.

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Section: Introductionmentioning

confidence: 99%

Solving a real-time allocation problem with constraint programming

Hladik

Cambazard

Déplanche

et al. 2008

Journal of Systems and Software

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“…To validate our scheduling algorithm, we also performed experiments on two real-time application programs presented in [28,29]. The first application consisted of a graph of 18 tasks and the second application consisted of a graph of 50 tasks.…”

Section: Results For Application Graphsmentioning

confidence: 99%

“…Random graphs have been widely used by researchers in the past to test the performance of their scheduling algorithms [17,24,27]. In addition, real-time application programs presented in [28,29] have also been tested. Three performance metrics have been used to evaluate the proposed scheme.…”

Section: Resultsmentioning

confidence: 99%

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A scalable task duplication based algorithm for improving the schedulability of real-time heterogeneous multiprocessor systems

Auluck

Agrawal

2003 International Conference on Parallel Processing Workshops, 2003. Proceedings.

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In this paper, we propose an O(v 2 ) scalable duplication based algorithm (RT-SDA) for scheduling precedence constrained real-time tasks on heterogeneous multiprocessors. This models a network of workstations, with processors of varying computing power. The algorithm takes the heterogeneities of both computation and communication of the multiprocessor system into account. RT-SDA employs selective task duplication to reduce the start time of the real-time tasks in the job, thereby increasing the guarantee ratio of the real-time application. Moreover, our scheme is scalable in that the application can be scheduled even if the available number of processors is less than the required number of processors. Compared to the existing scheduling algorithms in the literature, RT-SDA offers better schedulability in terms of a higher guarantee ratio.

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“…Other authors presented algorithmic approaches for assigning real-time tasks based on general purpose local search heuristics. Coli and Palazzari [6], DiNatale and Stankovic [7], Nicholson [15], and Tindell et al [22] gave algorithms based on simulated annealing to optimally allocate periodic tasks to resources. Algorithms based on genetic algorithms were proposed by Baccouche [5], Greenwood et al [10], and Sandnes [20].…”

Section: Introductionmentioning

confidence: 99%

Heuristic resource allocation algorithms for maximizing allowable workload in dynamic, distributed real-time systems

Juedes

Drews

Welch

et al.

18th International Parallel and Distributed Processing Symposium, 2004. Proceedings.

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This paper examines several heuristic algorithms for the maximum allowable workload (MAW) problem for realtime systems with tasks having variable workloads. Briefly, the problem concerns the allocation of n tasks to m processors, where each task t is characterized by a function t.r(w) that gives the running time of the task in terms of its workload (or input size) w. The objective of the maximum allowable workload problem is to find an allocation of tasks to processors so that the allocation is feasible (no task misses its deadline) when each task is given a workload of w or smaller and w is maximized. This optimization problem uses a robustness measure that is closely related to the MAIL (maximum allowable increase in load) metric recently proposed by Gertphol et. al. The main contribution of this paper is the the comparison of several heuristic algorithms for the MAW-RMS problem. Hillclimbing, random search, simulated annealing, and first-fit heuristics are presented and evaluated via simulation. As we show here, the first-fit greedy heuristic produces solutions of a reasonable quality compared to the other algorithms. In addition, we demonstrate the applicability of our model in air defense systems.

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A new method for optimization of allocation and scheduling in real time applications

Cited by 15 publications

References 15 publications

Solving a real-time allocation problem with constraint programming

Solving a real-time allocation problem with constraint programming

A scalable task duplication based algorithm for improving the schedulability of real-time heterogeneous multiprocessor systems

Heuristic resource allocation algorithms for maximizing allowable workload in dynamic, distributed real-time systems

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