The Complexity of Flowshop and Jobshop Scheduling

Garey, M. R.; Johnson, David S.; Sethi, Ravi

doi:10.1287/moor.1.2.117

Cited by 2,220 publications

(856 citation statements)

References 18 publications

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“…To provide context for this result, it is useful to consider the related literature from the flowshop scheduling community, e.g., [10,16,19,24]. In particular, the flowshop literature considers traditional tandem queues (which do not allow overlapping) with all jobs arrive at time zero and tend to focus on a different performance metric: makespan.…”

Section: Offline Schedulingmentioning

confidence: 99%

Joint optimization of overlapping phases in MapReduce

Lin

Zhang

Wierman

et al. 2013

Performance Evaluation

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MapReduce is a scalable parallel computing framework for big data processing. It exhibits multiple processing phases, and thus an efficient job scheduling mechanism is crucial for ensuring efficient resource utilization. This paper studies the scheduling challenge that results from the overlapping of the "map" and "shuffle" phases in MapReduce. We propose a new, general model for this scheduling problem, and validate this model using cluster experiments. Further, we prove that scheduling to minimize average response time in this model is strongly NP-hard in the offline case and that no online algorithm can be constant-competitive. However, we provide two online algorithms that match the performance of the offline optimal when given a slightly faster service rate (i.e., in the resource augmentation framework). Finally, we validate the algorithms using a workload trace from a Google cluster and show that the algorithms are near optimal in practical settings.

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Section: Offline Schedulingmentioning

confidence: 99%

Joint optimization of overlapping phases in MapReduce

Lin

Zhang

Wierman

et al. 2013

Performance Evaluation

View full text Add to dashboard Cite

show abstract

“…Our objective is to find a schedule π to minimizing the total flowtime for a time interval I. Actually, the classical two-stage flow shop problem (FlS) is an important special case of MapReduce scheduling problem, where there is only one map machine and one reduce machine, and FlS is known to be strongly NP-hard [28]. So, in this paper, we want to utilize some heuristics to improve our scheduling objective as far as possible.…”

Section: Schedulingmentioning

confidence: 99%

An Efficiency-Aware Scheduling for Data-Intensive Computations on MapReduce Clusters

Zhao

Yang

Fan

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYScheduling plays a key role in MapReduce systems. In this paper, we explore the efficiency of an MapReduce cluster running lots of independent and continuously arriving MapReduce jobs. Data locality and load balancing are two important factors to improve computation efficiency in MapReduce systems for data-intensive computations. Traditional cluster scheduling technologies are not well suitable for MapReduce environment, there are some in-used schedulers for the popular open-source Hadoop MapReduce implementation, however, they can not well optimize both factors. Our main objective is to minimize total flowtime of all jobs, given it's a strong NP-hard problem, we adopt some effective heuristics to seek satisfied solution. In this paper, we formalize the scheduling problem as job selection problem, a load balance aware job selection algorithm is proposed, in task level we design a strict data locality tasks scheduling algorithm for map tasks on map machines and a load balance aware scheduling algorithm for reduce tasks on reduce machines. Comprehensive experiments have been conducted to compare our scheduling strategy with well-known Hadoop scheduling strategies. The experimental results validate the efficiency of our proposed scheduling strategy.

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“…Johnson (1954) presented a seminal algorithm, referred to as Johnson's rule, that efficiently solves the problem of minimizing the makespan in a two-machine flow shop. Since the problem of minimizing the makespan in a flow shop with more than two machines is strongly NP-hard (Garey and Johnson, 1976), however, researchers have considered a flow shop with special structures for processing times, such as the ordered flow shop (Smith et al, 1975(Smith et al, , 1976 and the proportionate flow shop (Allahverdi, 1999;Hou and Hoogeveen, 2003;Ow, 1985). Smith et al (1975Smith et al ( , 1976 considered an m-machine ordered flow shop.…”

Section: Introductionmentioning

confidence: 99%

Min-Max Regret Version of an m-Machine Ordered Flow Shop with Uncertain Processing Times

Park¹,

Choi²

2015

Management Science and Financial Engineering

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We consider an m-machine flow shop scheduling problem to minimize the latest completion time, where processing times are uncertain. Processing time uncertainty is described through a finite set of processing time vectors. The objective is to minimize maximum deviation from optimality for all scenarios. Since this problem is known to be NPhard, we consider it with an ordered property. We discuss optimality properties and develop a pseudo-polynomial time approach for the problem with a fixed number of machines and scenarios. Furthermore, we find two special structures for processing time uncertainty that keep the problem NP-hard, even for two machines and two scenarios. Finally, we investigate a special structure for uncertain processing times that makes the problem polynomially solvable.

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The Complexity of Flowshop and Jobshop Scheduling

Cited by 2,220 publications

References 18 publications

Joint optimization of overlapping phases in MapReduce

Joint optimization of overlapping phases in MapReduce

An Efficiency-Aware Scheduling for Data-Intensive Computations on MapReduce Clusters

Min-Max Regret Version of an m-Machine Ordered Flow Shop with Uncertain Processing Times

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