By Ivan Ivanov; Marten J van Sinderen; Boris Shishkov
'Cloud Computing and providers technological know-how' contains a suite of the easiest papers offered on the overseas convention on Cloud Computing and providers technological know-how (CLOSER), which used to be held within the Netherlands in may well 2011.
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Extra resources for Cloud computing and services science
8 Summary and Future Development.......................................................... 36 References................................................................................................................ 37 17 18 Cloud Computing with e-Science Applications Summary This chapter discusses the challenges that are imposed by big data on the modern and future e-scientific data infrastructure (SDI). The chapter discusses the nature and definition of big data, including such characteristics as volume, velocity, variety, value, and veracity.
Published data that support one or another scientific hypothesis, research result, or statement. These data are typically linked to scientific publications as supplemental materials; they may be located on the publisher’s platform or authors’ institution platform and have open or licensed access. • Data linked and embedded into publications to support wide research consolidation, integration, and openness. Once the data are published, it is essential to allow other scientists to be able to validate and reproduce the data in which they are interested and possibly contribute new results.
3 Research Infrastructures and Infrastructure Requirements This section refers to and provides a short overview of different scientific communities, in particular as defined by the European Research Area (ERA) , to define requirements for the infrastructure facility, data-processing and management functionalities, user management, access control, and security. 1 Paradigm Change in Modern e-Science Modern e-science is moving to the data-intensive technologies that are becoming a new technology driver and require rethinking a number of infrastructure architecture and operational models, components, solutions, and processes to address the following general challenges [2, 4]: Cloud-Based Infrastructure for Data-Intensive e-Science Applications 25 • Exponential growth of data volume produced by different research instruments or collected from sensors • Need to consolidate e-infrastructures as persistent research platforms to ensure research continuity and cross-disciplinary collaboration, deliver/offer persistent services, with an adequate governance model The recent advancements in the general computer and big data technologies facilitate the paradigm change in modern e-science that is characterized by the following features: • Automation of all e-science processes, including data collection, storing, classification, indexing, and other components of the general data curation and provenance • Transformation of all processes, events, and products into digital form by means of multidimensional, multifaceted measurements, monitoring, and control; digitizing existing artifacts and other content • Possibility of reusing the initial and published research data with possible data repurposing for secondary research • Global data availability and access over the network for a cooperative group of researchers, including wide public access to scientific data • Existence of necessary infrastructure components and management tools that allow fast infrastructures and services composition, adaptation and provisioning on demand for specific research projects and tasks • Advanced security and access control technologies that ensure secure operation of the complex research infrastructures and scientific instruments and allow creating a trusted secure environment for cooperating groups and individual researchers The future SDI should support the whole data life cycle and explore the benefit of data storage/preservation, aggregation, and provenance on a large scale and during long or unlimited periods of time.