课题基金 / 基金详情

Cloud Open Source Research Mobility Network

Cloud Open Source Research Mobility Network
云开源研究移动网络
批准号:
EP/Y030346/1
负责人:
Peter Pietzuch
金额:
$7.58万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

项目摘要

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中文摘要
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英文摘要
CloudStars is an Open Source Research Mobility network in the field of Cloud Computing technology. The proposal combines elevenacademic institutions in nine European countries (Spain, Portugal, UK, Germany, Netherlands, Italy, Austria, Poland, Switzerland), twocompanies in Europe (SAP in Germany, NearBy Computing in Spain), and three industrial laboratories from IBM (USA, Switzerland, andIsrael). The major flow of secondments is scheduled between the academic institutions and IBM industrial labs, but also including asignificant number of secondments to SAP and NearBy Computing.CloudStars will create a global reference community in open source Cloud Computing. The participants will combine theoretical skillsandexperience in distributed systems research with industrial open source technologies and cutting-edge Cloud and Edgeinfrastructures. This will increase the overall global impact of research contributions, helping to arrive to millions of interested thirdparties through open source communities.The general goals of the project are:1. Increase the impact of European researchers with contributions to key open source projects and the involvement in open sourcecommunities.2. Rise the careers of European researchers through well-established networks both across EU and with global open source players.3. Increase the reproducibility of results in Science and Data Analytics through standard Cloud container technologies.Technical goals are:1. Development and benchmarking of next generation container technologies leveraging open source Cloud Native ComputingFoundation (CNCF) projects and GAIAX protocols.2. Design novel cloud serverless middleware over container technologies including Function as a Service, serverless containers, andevent-based orchestration.3. Apply novel machine learning techniques for managing containerized Cloud and Edge systems, involving the infrastructure andconfiguration of executions and services.
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