Specification and Runtime Workflow Support in the ASKALON Grid Environment

Prodan, Radu

doi:10.1155/2007/734021

Cited by 8 publications

(3 citation statements)

References 21 publications

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“…For example, the work in [1], [20], [31] [32,33], [34], [24], [28] and [30] are based on the sequential mapping approach. The sequential schemes have high computational complexity and consume significant amount of time and resources, especially when dealing with large workflows, such as brain imaging workflows, health care data analysis, and Pegasus workflow that tackle thousands of tasks [35][36][37].…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Parallel Scheduling Techniques for Network-on-Chip Based Systems

et al. 2021

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Minimizing execution time, energy consumption, and network load through scheduling algorithms is challenging for multi-processor-on-chip (MPSoC) based network-on-chip (NoC) systems. MPSoC based systems are prevalent in high performance computing systems. With the increase in computing capabilities of computing hardware, application requirements have increased many folds, particularly for real world scientific applications. Scheduling large scientific workflows consisting hundreds and thousands of tasks consume significant amount of time and resources. In this article, energy aware parallel scheduling techniques are presented primarily aimed at reducing the algorithm execution time while considering network load. Experimental results reveal that the proposed parallel scheduling algorithms achieve significant reduction in execution time.

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Section: Related Workmentioning

confidence: 99%

Energy Aware Parallel Scheduling Techniques for Network-on-Chip Based Systems

et al. 2021

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“…Different prediction approaches exist in order to estimate the performance of jobs running in clusters. Previous research analyses have focused on predicting execution time of jobs [12,13], waiting time [14,15] and slowdown time of applications spawned when resources are shared [16].…”

Section: Related Workmentioning

confidence: 99%

A History-Based Resource Manager for Genome Analysis Workflows Applications on Clusters with Heterogeneous Nodes

Badosa

Espinosa

Acevedo

et al. 2018

Int J Parallel Prog

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Bioinformatics workflows require large amounts of resources and are commonly executed in clusters. Determining the adequate amount of resources for bioinformatics applications is a tricky matter, since the resource usage of a single application might vary substantially from one execution to the next. Resource management systems in clusters don't consider these variations and subsequent needs. As a result, the computing power offered by clusters is not harnessed properly, compromising both application performance and resource efficiency. To tackle these issues, we propose a History-Based Resource Manager for bioinformatics workflows applications running on clusters with heterogeneous nodes. The proposed resource manager features a prediction model that generates multiple performance predictions for each job under different combinations of cluster resources. Furthermore, the proposed resource manager includes a scheduling algorithm that considers the degree of multiprogramming of the nodes, scheduling combinations of applications for simultaneous same-node execution upon their compatibility. To test the proposed resource manager, we process two workloads formed by different amounts of workflows made up by common bioinformatics applications. Results prove that for the given cases, the proposed resource manager improves the performance obtained with SLURM, using First Come First Served policy. The proposal shows an average workflow makespan improvement range between 28 and 35%, averaging 32%, an average workflow efficiency improvement range between 75 and 83%, averaging 79%, and an average resource usage improvement range between 96 and 101%, averaging 99%. Furthermore, the pro-Funded by the Spanish Economy ministry.

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“…Prodan [13] describes a generic system called ASKALON where the entire workflow (that is: the set of input data, the various steps in the computation and the set of output data, as well as the relation between these) is analysed and scheduled in such a way that optimal use is made of the available resources.…”

Section: Related Workmentioning

confidence: 99%

A Computational Framework for Flood Risk Assessment in The Netherlands

Markus

Courage

Mierlo

2010

Scientific Programming

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The safety of dikes in The Netherlands, located in the delta of the rivers Rhine, Meuse and Scheldt, has been the subject of debate for more than ten years. The safety (or flood risk) of a particular area may depend on the safety of other areas. This is referred to as effects of river system behaviour on flood risk (quantified as the estimated number of casualties and economic damage). A computational framework was developed to assess these effects. It consists of several components that are loosely coupled via data files and Tcl scripts to manage the individual programs and keep track of the state of the computations. The computations involved are lengthy (days or even weeks on a Linux cluster), which makes the framework currently more suitable for planning and design than for real-time operation. While the framework was constructedad hoc, it can also be viewed more formally as atuplespaceRealising this makes it possible to adopt the philosophy for other similar frameworks.

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Specification and Runtime Workflow Support in the ASKALON Grid Environment

Cited by 8 publications

References 21 publications

Energy Aware Parallel Scheduling Techniques for Network-on-Chip Based Systems

Energy Aware Parallel Scheduling Techniques for Network-on-Chip Based Systems

A History-Based Resource Manager for Genome Analysis Workflows Applications on Clusters with Heterogeneous Nodes

A Computational Framework for Flood Risk Assessment in The Netherlands

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