AMOS: Using the Cloud for On-Demand Execution of e-Science Applications

Strijkers, Rudolf; Toorop, Willem; Hoof, Alain van; Grosso, Paola; Belloum, Adam; Vasuining, Dmitry; Laat, Cees de; Meijer, Robert

doi:10.1109/escience.2010.15

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…With a fundamentally different resource management philosophy based on-demand resource provisioning, clouds are seen as particularly useful for applications where the results of computations must be obtained within a specified time, such as simulation-based urgent clinical decision-making [20].…”

Section: Related Workmentioning

confidence: 99%

Execution Management and Efficient Resource Provisioning for Flood Decision Support

Baliś

Kasztelnik

Malawski

et al. 2015

Procedia Computer Science

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We present a resource provisioning and execution management solution for a flood decision support system. The system, developed within the ISMOP project, features an urgent computing scenario in which flood threat assessment for large sections of levees is requested within a specified deadline. Unlike typical decision support systems which utilize heavyweight simulations in order to predict the possible course of an emergency, in ISMOP we employ an alternative approach based on the 'scenario identification' method. We show that this approach is a particularly good fit for the resource provisioning model of IaaS Clouds. We describe the architecture of the ISMOP decision support system, focusing on the urgent computing scenario and its formal resource provisioning model. Preliminary results of experiments performed in order to calibrate and validate the model indicate that the model fits experimental data.

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Section: Related Workmentioning

confidence: 99%

Execution Management and Efficient Resource Provisioning for Flood Decision Support

Baliś

Kasztelnik

Malawski

et al. 2015

Procedia Computer Science

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“…a virtual Grid on top of the Cloud resources using their legacy systems and execute workflows. A system, AMOS (Strijkers et al, 2010), presents a layer on top of a workflow management system and dynamically creates resources on the Cloud to instantiate a transient Grid ready for immediate use in the Cloud. A similar approach has also been discussed and tested by (Vo¨ckler et al, 2011;Juve et al, 2009).…”

Section: Workflow Execution Scenario On the Cloudmentioning

confidence: 99%

Cloud infrastructure provenance collection and management to reproduce scientific workflows execution

Hasham

Munir

McClatchey

2018

Future Generation Computer Systems

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The emergence of Cloud computing provides a new computing paradigm for scientific workflow execution. It provides dynamic, on-demand and scal-able resources that enable the processing of complex workflow-based experiments. With the ever growing size of the experimental data and increasingly complex processing workflows, the need for reproducibility has also become essential. Provenance has been thought of a mechanism to verify a workflow and to provide workflow reproducibility. One of the obstacles in reproduc-ing an experiment execution is the lack of information about the execution infrastructure in the collected provenance. This information becomes crit-ical in the context of Cloud in which resources are provisioned on-demand and by specifying resource configurations. Therefore, a mechanism is re-quired that enables capturing of infrastructure information along with the provenance of workflows executing on the Cloud to facilitate the re-creation of execution environment on the Cloud. This paper presents a framework, ReCAP, along with the proposed mapping approaches that aid in captur-ing the Cloudaware provenance information and help in re-provisioning the execution resource on the Cloud with similar configurations. Experimental evaluation has shown the impact of different resource configurations on the workflow execution performance, therefore justifies the need for collecting such provenance information in the context of Cloud. The evaluation has also demonstrated that the proposed mapping approaches can capture Cloud information in various Cloud usage scenarios without causing performance overhead and can also enable the re-provisioning of resources on Cloud. Experiments were conducted using workflows from different scientific domains such as astronomy and neuroscience to demonstrate the applicability of this research for different workflows.

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“…The latter is particularly important when using a public cloud and quirks in the charging models of cloud service providers can make the question of efficient usage and costing more complex than it would appear at first sight [11]. As a consequence, a common approach taken is to deploy a traditional batch job submission system (e.g., [23][24][25]) or a workflow management system (e.g., [26,27]) into the cloud or to use cloud resources to extend an existing cluster or grid into the cloud through 'cloud-bursting' (e.g., [28]). A number of clustering and grid environments have been ported to cloud infrastructures, often provided as a set of instance images that can be deployed relatively easily.…”

Section: Job Managementmentioning

confidence: 99%

An elastic virtual infrastructure for research applications (ELVIRA)

Voß

Barker

Asgari-Targhi

et al. 2013

Journal of Cloud Computing: Advances, Systems and Applications

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Cloud computing infrastructures provide a way for researchers to source the computational and storage resources they require to conduct their work and to collaborate within distributed research teams. We provide an overview of a cloud-based elastic virtual infrastructure for research applications that we have established to provide researchers with a collaborative research environment that automatically allocates cloud resources as required. We describe how we have used this infrastructure to support research on the Sun's corona and how the elasticity provided by cloud infrastructures can be leveraged to provide high-throughput computing resources using a set of off-the-shelf technologies and a small number of additional tools that are simple to deploy and use. The resulting infrastructure has a number of advantages for the researchers compared to traditional clusters or grid computing environments that we discuss in the conclusions.

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AMOS: Using the Cloud for On-Demand Execution of e-Science Applications

Cited by 11 publications

References 12 publications

Execution Management and Efficient Resource Provisioning for Flood Decision Support

Execution Management and Efficient Resource Provisioning for Flood Decision Support

Cloud infrastructure provenance collection and management to reproduce scientific workflows execution

An elastic virtual infrastructure for research applications (ELVIRA)

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