Scheduling Tasks with AND/OR Precedence Constraints

Gillies, Donald; Liu, Jane W. S.

doi:10.1137/s0097539791218664

Cited by 52 publications

(20 citation statements)

References 11 publications

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“…Mohring and Stork (2000) have introduced a very useful representation of pre-selective policies using so-called waiting conditions. Waiting conditions can be modelled as AND/OR precedence constraints (Gillies and Liu (1995), Mohring et al (2000)). A waiting condition is given by a pair (X,j), Xc V,j E V \)(, where activity j cannot be started before at least one activity i E X has finished.…”

Section: Stochastic Resource-constrained Project Schedulingmentioning

confidence: 99%

Project scheduling under uncertainty: Survey and research potentials

Herroelen

Leus

2005

European Journal of Operational Research

753

387

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The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling; fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.

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Section: Stochastic Resource-constrained Project Schedulingmentioning

confidence: 99%

Project scheduling under uncertainty: Survey and research potentials

Herroelen

Leus

2005

European Journal of Operational Research

753

387

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“…An intuitive motivation for and/or precedence constraints is noted by Gillies and Liu [12]. An engine head has to be fixed by four bolts.…”

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confidence: 99%

Scheduling with AND/OR Precedence Constraints

Möhring¹,

Skutella

Stork³

2004

SIAM J. Comput.

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Abstract. In many scheduling applications it is required that the processing of some job be postponed until some other job, which can be chosen from a pregiven set of alternatives, has been completed. The traditional concept of precedence constraints fails to model such restrictions. Therefore, the concept has been generalized to so-called and/or precedence constraints which can cope with this kind of requirement. In the context of traditional precedence constraints, feasibility, transitivity, and the computation of earliest start times for jobs are fundamental, well-studied problems. The purpose of this paper is to provide efficient algorithms for these tasks for the more general model of and/or precedence constraints. We show that feasibility as well as many questions related to transitivity can be solved by applying essentially the same linear-time algorithm. In order to compute earliest start times we propose two polynomial-time algorithms to cope with different classes of time distances between jobs.

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“…Several wellknown scheduling schemes, such as rate-monotonic [12,16] and earliest-deadline-first (EDF), exist for scheduling real-time tasks in an uniprocessor environment. Gillies [8] has studied the scheduling of tasks in AND/OR graphs, a problem very similar to our problem of scheduling task chains in tunable applications. However, the validity of most of these results is restricted to either uniprocessor systems or a sequential task model.…”

Section: Discussion and Related Workmentioning

confidence: 99%

“…Defense Advanced Research Projects Agency,3701 North Fairfax Drive,Arlington,VA,22203-1714 8. PERFORMING ORGANIZATION REPORT NUMBER…”

Section: Performing Organization Name(s) and Address(es)mentioning

confidence: 99%

Exploiting Application Tunability for Efficient, Predictable Parallel Resource Management

Chang¹,

Karamcheti²,

Kedem³

1998

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Parallel computing is becoming increasing central and mainstream, driven both by the widespread availability of commodity SMP and high-performance cluster platforms, as well as the growing use of parallelism in general-purpose applications such as image recognition, virtual reality, and media processing. In addition to performance requirements, the latter computations impose soft real-time constraints, necessitating efficient, predictable parallel resource management. Unfortunately, traditional resource management approaches in both parallel and real-time systems are inadequate for meeting this objective; the parallel approaches focus primarily on improving application performance and/or system utilization at the cost of arbitrarily delaying a given application, while the real-time approaches are overly conservative sacrificing system utilization in order to meet application deadlines.In this paper, we propose a novel approach for increasing parallel system utilization while meeting application soft real-time deadlines. Our approach exploits the application tunability found in several general-purpose computations. Tunability refers to an application's ability to trade off resource requirements over time, while maintaining a desired level of output quality. In other words, a large allocation of resources in one stage of the computation's lifetime may compensate, in a parameterizable manner, for a smaller allocation in another stage. We first describe language extensions to support tunability in the Calypso programming system, a component of the MILAN metacomputing project, and evaluate their expressiveness using an image processing application. We then characterize the performance benefits of tunability, using a synthetic task system to systematically identify its benefits and shortcomings. Our results are very encouraging: application tunability is convenient to express, and can significantly improve parallel system utilization for computations with predictability requirements.

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Scheduling Tasks with AND/OR Precedence Constraints

Cited by 52 publications

References 11 publications

Project scheduling under uncertainty: Survey and research potentials

Project scheduling under uncertainty: Survey and research potentials

Scheduling with AND/OR Precedence Constraints

Exploiting Application Tunability for Efficient, Predictable Parallel Resource Management

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