CYCLONE: A Platform for Data Intensive Scientific Applications in Heterogeneous Multi-cloud/Multi-provider Environment

Demchenko, Yuri; Blanchet, Christophe; Loomis, Charles; Branchat, Rob; Slawick, Mathias; Zilci, Ilke; Bedri, Mohamed; Gibrat, Jean‐François; Lodygensky, Oleg; Živković, Miroslav; Laat, Cees de; Baranda, José Ignacio Aznar

doi:10.1109/ic2ew.2016.46

Cited by 17 publications

(8 citation statements)

References 11 publications

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“…under the FAIR principle [2] and/or as Linked Open Data as suggested by Tim Berners-Lee [3]). This sounds nice but it still leaves rules, for example to properly acknowledge sources and to protect personal and commercially sensitive data, even within collaborating communities [4]. Moreover, this doesn't solve (or even decrease) the prevalent polarization: data are either completely public (with one or a few wellknown commonly agreed governance rules) or completely under control with heterogeneous (yet potentially similar) governance rules written in different languages, similar to the situation for copyright licenses.…”

Section: Introductionmentioning

confidence: 99%

Towards a Computer-Interpretable Actionable Formal Model to Encode Data Governance Rules

Rui

Atkinson

2019

2019 15th International Conference on eScience (eScience)

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With the needs of science and business, data sharing and re-use has become an intensive activity for various areas. In many cases, governance imposes rules concerning data use, but there is no existing computational technique to help data-users comply with such rules. We argue that intelligent systems can be used to improve the situation, by recording provenance records during processing, encoding the rules and performing reasoning. We present our initial work, designing formal models for data rules and flow rules and the reasoning system, as the first step towards helping data providers and data users sustain productive relationships.

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Section: Introductionmentioning

confidence: 99%

Towards a Computer-Interpretable Actionable Formal Model to Encode Data Governance Rules

Rui

Atkinson

2019

2019 15th International Conference on eScience (eScience)

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“…The paper refers to the previous authors works that researched new approaches to building effective curricula in Cloud Computing, Big Data and Data Science [6, 7. 8, 9] and discussed cloud automation platforms [10] that all can be used for creating modern Data Science Education Environment.…”

Section: Introductionmentioning

confidence: 99%

Customisable Data Science Educational Environment: From Competences Management and Curriculum Design to Virtual Labs On-Demand

Demchenko

Belloum

Laat

et al. 2017

2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

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Data Science is an emerging field of science, which requires a multidisciplinary approach and is based on the Big Data and data intensive technologies that both provide a basis for effective use of the data driven research and economy models. Modern data driven research and industry require new types of specialists that are capable to support all stages of the data lifecycle from data production and input to data processing and actionable results delivery, visualisation and reporting, which can be jointly defined as the Data Science professions family. The education and training of Data Scientists currently lacks a commonly accepted, harmonized instructional model that reflects all multidisciplinary knowledge and competences that are required from the Data Science practitioners in modern, data driven research and the digital economy. The educational model and approach should also solve different aspects of the future professionals that includes both theoretical knowledge and practical skills that must be supported by corresponding education infrastructure and educational labs environment. In modern conditions with the fast technology change and strong skills demand, the Data Science education and training should be customizable and delivered in multiple form, also providing sufficient data labs facilities for practical training. This paper discussed both aspects: building customizable Data Science curriculum for different types of learners and proposing a hybrid model for virtual labs that can combine local university facility and use cloud based Big Data and Data analytics facilities and services on demand. The proposed approach is based on using the EDISON Data Science Framework (EDSF) developed in the EU funded Project EDISON and CYCLONE cloud automation systems being developed in another EU funded project CYCLONE.

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“…Both data-intensive and user-intensive software applications could be considered as compute-intensive applications. This is because they often require continuously increasing power of computing resources and storage volume that are in many cases required ondemand for specific operations in data lifecycle [17]. This therefore calls for a capacity planning approach as suggested by [18] in most organizations that are not in a position to purchase additional computational resources each time need arises due to financial constrains.…”

Section: Resource-intensive Applicationsmentioning

confidence: 99%

Application Resource Management for Highly Computational Applications in the Operational Environment: A Critical Review

Balikuddembe¹,

Gudu²

2017

JSEA

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Computational resources have such a significant influence on the operation of any software application, it is therefore important to understand how these applications utilize these resources. Modern resource-intensive enterprise and scientific applications are creating a growing demand for high performance computing infrastructures. They constantly interact with and rely heavily on complex resources. However, they often operate in resource-limited environments yet they often handle massive data, both in size and complexity. Software application services, processes or transactions compete for the much required but scarce resources. This creates the need to improve the existing resource allocation and management issue in such operational environments, as well as propose new ones, if necessary. Software developers try to analyze application operation environment using diverse analysis and design methods. Our aim therefore, is to design a tool that is able to work with a hybrid of adaptive and prediction-based resource management and allocation models while applying the priority based job scheduling algorithm to try and solve the application resource management challenges currently being faced in such environments, even if, partially.

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CYCLONE: A Platform for Data Intensive Scientific Applications in Heterogeneous Multi-cloud/Multi-provider Environment

Cited by 17 publications

References 11 publications

Towards a Computer-Interpretable Actionable Formal Model to Encode Data Governance Rules

Towards a Computer-Interpretable Actionable Formal Model to Encode Data Governance Rules

Customisable Data Science Educational Environment: From Competences Management and Curriculum Design to Virtual Labs On-Demand

Application Resource Management for Highly Computational Applications in the Operational Environment: A Critical Review

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