A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues

Xu, Yuming; Li, Kenli; Hu, Jingtong; Li, Keqin

doi:10.1016/j.ins.2014.02.122

Cited by 348 publications

(148 citation statements)

References 54 publications

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“…GAs are natural parallel evolution algorithms and can greatly improve the computing efficiency in a good parallel environment. Xu et al, 16 Chandio et al, 17 and Xu et al 18 studied on the parallel computing system for task scheduling problem and analyzed the efficiency. With the rapid development of the hardware, an increasing number of people began to use graphics processing unit (GPU) for computing.…”

Section: Literature Reviewmentioning

confidence: 99%

A three-layer parallel computing system for shipbuilding project scheduling optimization

Han

Yang

et al. 2017

Advances in Mechanical Engineering

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To solve the large-scale scheduling problem more efficiently within the requirements of the contract in shipyard, a threelayer parallel computing system was proposed. An optimized model for shipbuilding project scheduling problem was constructed under the condition of taking time and resource constraints into account. Moreover, the key techniques of proposed system were elaborated and the main steps were designed. In the first computing layer, the problem was decomposed into small parts in heterogeneous systems, reducing the problem scale; then, in the second layer, a coevolution strategy for multi-populations was put forward to improve the algorithm robustness; in the third layer, a massive parallel computing method was performed under the Graphic Processing Unit structure. Finally, through two simulation examples, the robustness and outperforming others of the improved algorithm were verified.

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Section: Literature Reviewmentioning

confidence: 99%

A three-layer parallel computing system for shipbuilding project scheduling optimization

Han

Yang

et al. 2017

Advances in Mechanical Engineering

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“…The HGS-Sched enabled a simultaneous search of solution space by many small dependent populations. In comparison with mono-population and hybrid genetic-based schedulers the proposed algorithm proved highly effective and resulted in fast reductions in makespan and flowtime.In [32] authors proposed Multiple Priority Queues Genetic Algorithm (MPQGA) which incorporated a genetic algorithm (GA) approach which is suitable for scenario of Directed Acyclic Graph (DAG) scheduling designed with Crossover, Mutation and Fitness function. Proposal exploited the improvement of both Evolutionary-based and Heuristicbased algorithms while avoiding their drawbacks.…”

Section: Current and Historic Gas Proposalsmentioning

confidence: 99%

Current Perspective in Task Scheduling Techniques in Cloud Computing: A Review

Thaman¹,

Singh²

2016

IJFCST

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“…At present, the problem of multiprocessor scheduling for a DAG task is approaching, and the research on different target resources, scheduling objectives and scheduling methods is approaching maturity [6][7]. With the rapid development of grid computing and cloud computing applications, in a heterogeneous distributed computing environment on how to improve the multiple task scheduling performance aspects put forward the new requirements, also caused the wide attention of scholars both at home and abroad [8][9]. For multiprocessor task scheduling, how to improve the throughput of multi-task scheduling system is a research hotspot [10].…”

Section: Introductionmentioning

confidence: 99%

A novel HEFTS-L algorithm for scheduling large number of DAG tasks

Liu¹,

Xu²,

Cheng³

et al. 2017

Proceedings of the 3rd Workshop on Advanced Research and Technology in Industry (WARTIA 2017)

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Abstract:In recent years, along with the development of technologies for distributed computing such as big data and clouds workflow systems, efficiency of workflow scheduling has become very impotent. Hence scheduling of multiple DAGs sharing on heterogeneous distributed resources has attracted intensive attention recently. This paper issues on scheduling of multiple DAGs with Deadline constraints surrounding scheduling of multiple DAGs sharing on heterogeneous distributed resources on traditional DAG-based task. It brings forward two parameter association methods to balance DAG Deadline constraint priority including relative strictness and laxity, which are used to balance priority of multiple DAGs. An improved HEFT algorithm based on priority was put forward to schedule multiple DAGs with deadline constraint. Through experiments, the phenomenon of "overload" can be detected due to the high degree of emergency of DAG scheduling. Once a task was detected, it can be selective discarded with corresponding measures, so as to maximize DAG before deadline.

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A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues

Cited by 348 publications

References 54 publications

A three-layer parallel computing system for shipbuilding project scheduling optimization

A three-layer parallel computing system for shipbuilding project scheduling optimization

Current Perspective in Task Scheduling Techniques in Cloud Computing: A Review

A novel HEFTS-L algorithm for scheduling large number of DAG tasks

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