Infrastructure Aware Scientific Workflows and Their Support by a Science Gateway

Kacsuk, Péter; Kecskeméti, Gábor; Kertész, Attila; Németh, Zsolt; Visegrádi, Ádám; Gergely, Mark

doi:10.1109/iwsg.2015.14

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…In general, we can call the above concept as an infrastructure aware workflow, as introduced in [22]. Infrastructure aware workflows consist of the above described three steps (deploy, execute, release) and can be implemented as one or as multiple workflow nodes, as it will be demonstrated later in this paper.…”

Section: Proposed Generic Approachmentioning

confidence: 99%

“…When compared to the work in [22], this paper does not utilize the infrastructure aware workflow concept to address workflow interoperability, but it applies the same concept to integrate applications requiring specific external execution environments to workflow systems. The method is generic in the sense that once components of the infrastructure aware workflow are implemented as applications, these components can be easily embedded and called from any workflow system.…”

Section: Proposed Generic Approachmentioning

confidence: 99%

“…The integration of Big Data processing frameworks and solutions, such as MapReduce and Hadoop, to science gateways and workflows could take the burden of setting-up and managing these computation environments from domain scientists and facilitate the wider take-up of such technologies. This paper describes a generic approach based on infrastructure aware workflows [22] when integrating workflow-based science gateway frameworks with Big Data processing. It also evaluates a concrete implementation of this generic approach when integrating Hadoop with the WS-PGRADE/gUSE gateway and workflow solution.…”

Section: Introductionmentioning

confidence: 99%

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Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

et al. 2016

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Cloud computing offers massive scalability and elasticity required by many scientific and commercial applications. Combining the computational and data handling capabilities of clouds with parallel processing also has the potential to tackle Big Data problems efficiently. Science gateway frameworks and workflow systems enable application developers to implement complex applications and make these available for end-users via simple graphical user interfaces. The integration of such frameworks with Big Data processing tools on the cloud opens new opportunities for application developers. This paper investigates how workflow systems and science gateways can be extended with Big Data processing capabilities. A generic approach based on infrastructure aware workflows is suggested and a proof of concept is implemented based on the WS-PGRADE/gUSE science gateway framework and its integration with the Hadoop parallel data processing solution based on the MapReduce paradigm in the cloud. The provided analysis demonstrates that the methods described to

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Section: Proposed Generic Approachmentioning

confidence: 99%

Section: Proposed Generic Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

et al. 2016

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“…608886 (CloudSME). This paper is a significantly revised and extended version of the workshop paper [32].…”

Section: Acknowledgmentmentioning

confidence: 99%

Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

et al. 2016

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Abstract-The workflow interoperability problem was successfully solved by the SHIWA project if the workflows to be integrated were running in the same grid infrastructure. However, in the more generic case when the workflows were running in different infrastructures the problem has not been solved yet. In the current paper we show a solution for this problem by introducing a new type of workflow called infrastructure-aware workflow. These are scientific workflows extended with new node types that enable the on-the-fly creation and destruction of the required infrastructures in the clouds. The paper shows the semantics of these new types of nodes and workflows and also how they can solve the workflow interoperability problem. The paper also describes how these new type of workflows can be implemented by a new service called Occopus, and how this service can be integrated with the existing SHIWA Simulation Platform services like the WS-PGRADE/gUSE portal to provide the required functionalities of solving the workflow interoperability problem.

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“…Recently a new feature has been introduced in this gateway by enabling a new type of workflow called infrastructure-aware workflow [28]. These are scientific workflows extended with new node types that enable the on-the-fly creation and destruction of the required infrastructures in the clouds.…”

Section: Towards Integrating the Approach To A Scientific Gatewaymentioning

confidence: 99%

Towards Enabling Scientific Workflows for the Future Internet of Things

Kertész

Pflanzner

2016

Internet of Things. IoT Infrastructures

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Abstract. Cloud computing offers on-demand access to computational, infrastructure and data resources operated from a remote source. This novel technology has opened new ways of flexible resource provisions for businesses to manage applications and data responding to new demands from customers. In the current web application scenario a rapidly growing number of powerful devices join the Internet, significantly impacting on the global traffic volume and foreshadowing a world of smart devices, or things in the Internet of Things (IoT) perspective. This trend calls for an ecosystem that provides means to interconnect and control these devices. In this position paper we envision the integration of IoT into Cloudenabled scientific workflows to support the proliferation of IoT with the help of cloud technologies. These enhanced workflows will enable the creation and management of user applications that bring clouds and IoT closer to users by hiding the complexity and cumbersome utilization of virtualized resources, data sources and things. The goal of this approach is to ease the lives of users and foster scientific work by engaging the Internet of Things.

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Infrastructure Aware Scientific Workflows and Their Support by a Science Gateway

Cited by 8 publications

References 11 publications

Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

Extending Science Gateway Frameworks to Support Big Data Applications in the Cloud

Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

Towards Enabling Scientific Workflows for the Future Internet of Things

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