Investigation of the DAG eligible jobs maximization algorithm in a grid

Szepieniec, Tomasz; Bubak, Marian

doi:10.1109/grid.2008.4662819

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…They consider only the DAG structure to improve application performance. While in the evaluation of Szepieniec et al [149] showed that the effectiveness is limited without considering task execution time.…”

Section: Robust Workflow Schedulingmentioning

confidence: 99%

Workflow scheduling in cloud: a survey

2015

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To program in distributed computing environments such as grids and clouds, workflow is adopted as an attractive paradigm for its powerful ability in expressing a wide range of applications, including scientific computing, multi-tier Web, and big data processing applications. With the development of cloud technology and extensive deployment of cloud platform, the problem of workflow scheduling in cloud becomes an important research topic. The challenges of the problem lie in: NP-hard nature of task-resource mapping; diverse QoS requirements; on-demand resource provisioning; performance fluctuation and failure handling; hybrid resource scheduling; data storage and transmission optimization. Consequently, a number of studies, focusing on different aspects, emerged in the literature. In this paper, we firstly conduct taxonomy and comparative review on workflow scheduling algorithms. Then, we make a comprehensive survey of workflow scheduling in cloud environment in a problem-solution manner. Based on the analysis, we also highlight some research directions for future investigation.

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Section: Robust Workflow Schedulingmentioning

confidence: 99%

Workflow scheduling in cloud: a survey

2015

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“…Simulation experiments carried out in [14] indicate that ICO significantly improves the execution time of a large class of DAGs over three simple, intuitively compelling scheduling heuristics. Malewicz et al [9] extended the ICO algorithm to a practical heuristic applied in the Condor Project [11], and the usefulness of its implementation was assessed in [12], [21].…”

Section: Related Workmentioning

confidence: 99%

A Priority-Based Scheduling Heuristic to Maximize Parallelism of Ready Tasks for DAG Applications

Zheng

Tang

Sakellariou

2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Abstract-In practical Cloud/Grid computing systems, DAG scheduling may be faced with challenges arising from severe uncertainty about the underlying platform. For instance, it could be hard to have explicit information about task execution time and/or the availability of resources; both may change dynamically, in difficult to predict ways. In such a setting, the development of various kinds of just-in-time scheduling schemes, which aim at maximizing the parallelism of ready tasks of DAG, seems to be a promising approach to cope with the lack of environment information and achieve efficient DAG execution. Although many attempts have been tried to develop such just-in-time scheduling heuristics, most of them are based on DAG decomposition, which results in complicated and suboptimal solutions for general DAGs. This paper presents a priority-based heuristic, which is not only easy to apply to arbitrary DAGs, but also exhibits comparable or better performance than the existing solutions.

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“…Several algorithms have been proposed for different types of Grids. This includes incremental workflow partitioning and full graph scheduling strategies [7], scheduling with communication and processing variations [8], QoS workflow scheduling with deadline and budget constraints [9], scheduling data intensive workflows [10], opportunistic workflow scheduling [11], and level based task clustering of data intensive workflows on computational grids [12].…”

Section: Related Workmentioning

confidence: 99%

Multiple Workflow Scheduling Strategies with User Run Time Estimates on a Grid

et al. 2012

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In this paper, we present an experimental study of deterministic non-preemptive multiple workflow scheduling strategies on a Grid. We distinguish twenty five strategies depending on the type and amount of information they require. We analyze scheduling strategies that consist of two and four stages: labeling, adaptive allocation, prioritization, and parallel machine scheduling. We apply these strategies in the context of executing the Cybershake, Epigenomics, Genome, Inspiral, LIGO, Montage, and SIPHT workflows applications. In order to provide performance comparison, we performed a joint analysis considering three metrics. A case study is given and corresponding results indicate that well known DAG scheduling algorithms designed for single DAG and single machine settings are not well suited for Grid scheduling scenarios, where user run time estimates are available. We show that the proposed new strategies outperform other strategies in terms of approximation factor, mean critical path waiting time, and critical path slowdown. The robustness of these strategies is also discussed.

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Investigation of the DAG eligible jobs maximization algorithm in a grid

Cited by 8 publications

References 14 publications

Workflow scheduling in cloud: a survey

Workflow scheduling in cloud: a survey

A Priority-Based Scheduling Heuristic to Maximize Parallelism of Ready Tasks for DAG Applications

Multiple Workflow Scheduling Strategies with User Run Time Estimates on a Grid

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