Scientific Workflow Makespan Reduction through Cloud Augmented Desktop Grids

Reynolds, Christopher J.; Winter, Stephen; Terstyánszky, Gábor; Kiss, Tamás; Greenwell, Pamela; Ács, Sándor; Kacsuk, Péter

doi:10.1109/cloudcom.2011.13

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…In addition, a particular grid, i.e. desktop grid, exists for scientific workflow execution [117]. Compared to cluster computing, grid computing gathers heterogeneous computer resources to provide more flexible services to diverse users by inner resource allocation mechanisms.…”

Section: Infrastructure Layermentioning

confidence: 99%

A Survey of Data-Intensive Scientific Workflow Management

et al. 2015

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International audienceNowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A data-intensive scientific workflow is useful for modeling such pro- cess. Since the sequential execution of data-intensive scientific workflows may take much time, Scientific Workflow Management Systems (SWfMSs) should enable the parallel execution of data-intensive scientific workflows and exploit the resources distributed in different infrastruc- tures such as grid and cloud. This paper provides a survey of data-intensive scientific workflow management in SWfMSs and their parallelization techniques. Based on a SWfMS functional ar- chitecture, we give a comparative analysis of the existing solutions. Finally, we identify research issues for improving the execution of data-intensive scientific workflows in a multisite cloud

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Section: Infrastructure Layermentioning

confidence: 99%

A Survey of Data-Intensive Scientific Workflow Management

et al. 2015

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“…As it was described in Section 1, the authors of this paper have also implemented solutions for cluster‐based SG and DG systems that automated several steps of the docking process and made the simulations available from high‐level science gateway interfaces [25] [26]. The performance of the DG has been further enhanced in by utilising dedicated private cloud resources that successfully cut the ‘long tail’ of work units responsible for very long overall execution times on DGs. However, the application of cloud computing resources for molecular docking experiments is in relatively early stages.…”

Section: Related Workmentioning

confidence: 99%

Large‐scale virtual screening experiments on Windows Azure‐based cloud resources

Kiss

Borsody

Terstyánszky

et al. 2013

Concurrency and Computation

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SUMMARYMolecular docking simulations have high potential to contribute to a wide area of molecular and biomedical research in various disciplines including molecular biology, drug design, environmental studies and psychology. Conducting large‐scale molecular docking experiments requires a vast amount of computing resources. Several types of distributed computing infrastructures have been investigated and utilized recently to conduct such simulations, including service and desktop grid systems or local clusters. This paper investigates and analyses how Windows Azure‐based cloud resources can be applied for this purpose. A virtual screening experiment framework has been implemented on a Windows Azure‐based cloud using the generic worker concept. Virtual machines can be instantiated in the cloud on demand scaling up the simulations based on the volume of molecules to be docked and the available financial resources. Bioscientists are able to execute the simulations and visualise the results from a high‐level user interface. The paper describes the experiences when implementing the molecular docking application on this novel platform and provides the first benchmarking experiments to evaluate the suitability of the infrastructure for computation intensive simulations. Copyright © 2013 John Wiley & Sons, Ltd.

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“…Reynolds et al [10] proposed the utilization of Clouds to complement desktop Grid resources. However, Cloud resources are deployed with the goal of replicating slow tasks to increase the chance of an early completion of the workflow.…”

Section: Related Workmentioning

confidence: 99%

Meeting Deadlines of Scientific Workflows in Public Clouds with Tasks Replication

Calheiros

Buyya

2014

IEEE Trans. Parallel Distrib. Syst.

205

118

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Abstract-The elasticity of Cloud infrastructures makes them a suitable platform for execution of deadline-constrained workflow applications, because resources available to the application can be dynamically increased to enable application speedup. Existing research in execution of scientific workflows in Clouds either try to minimize the workflow execution time ignoring deadlines and budgets or focus on the minimization of cost while trying to meet the application deadline. However, they implement limited contingency strategies to correct delays caused by underestimation of tasks execution time or fluctuations in the delivered performance of leased public Cloud resources. To mitigate effects of performance variation of resources on soft deadlines of workflow applications, we propose an algorithm that uses idle time of provisioned resources and budget surplus to replicate tasks. Simulation experiments with four well-known scientific workflows show that the proposed algorithm increases the likelihood of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.

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Scientific Workflow Makespan Reduction through Cloud Augmented Desktop Grids

Cited by 13 publications

References 13 publications

A Survey of Data-Intensive Scientific Workflow Management

A Survey of Data-Intensive Scientific Workflow Management

Large‐scale virtual screening experiments on Windows Azure‐based cloud resources

Meeting Deadlines of Scientific Workflows in Public Clouds with Tasks Replication

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