Desktop Grids, such as XtremWeb and BOINC, and Service Grids, such as EGEE, are two different approaches for science communities to gather computing power from a large number of computing resources. Nevertheless, little work has been done to combine these two Grid technologies in order to establish a seamless and vast Grid resource pool. In this paper we present the EGEE Service Grid, the BOINC and XtremWeb Desktop Grids. Then, we present the EDGeS solution to bridge the EGEE Service Grid with the BOINC and XtremWeb Desktop Grids

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This review article summarizes the history the Hungarian Scientific Cloud Infrastructure project. This research infrastructure was launched officially on 1 October 2016, funded by the Hungarian Academy of Sciences. With the support of ELKH, the infrastructure’s capacity has been substantially boosted; the features and workflows that it offers to scientists were significantly expanded to celebrate the arrival of the year 2022. The article reviews the types of work Hungarian researchers implemented on the infrastructure, thereby providing an overview of the state of cloud-computing enabled science in Hungary.

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Service grids and desktop grids are both promoted by their supportive communities as great solutions for solving the available compute power problem and helping to balance loads across network systems. Little work, however, has been undertaken to blend these two technologies together. In this paper we introduce a new EU project, that is building technological bridges to facilitate service and desktop grid interoperability. We provide a taxonomy and background into service grids, such as EGEE and desktop grids or volunteer computing platforms, such as BOINC and XtremWeb. We then describe our approach for identifying translation technologies between service and desktop grids. The individual themes discuss the actual bridging technologies employed and the distributed data issues surrounding deployment.

Reference architectures for big data and machine learning include not only interconnected building blocks but important considerations (among others) for scalability, manageability and usability issues as well. Leveraging on such reference architectures, the automated deployment of distributed toolsets and frameworks on various clouds is still challenging due to the diversity of technologies and protocols. The paper focuses particularly on the widespread Apache Spark cluster with Jupyter as the particularly addressed framework, and the Occopus cloud-agnostic orchestrator tool for automating its deployment and maintenance stages. The presented approach has been demonstrated and validated with a new, promising text classification application on the Hungarian academic research infrastructure, the OpenStack-based MTA Cloud. The paper explains the concept, the applied components, and illustrates their usage with real use-case measurements. K E Y W O R D S big data, cloud, machine learning, parallel and distributed execution, reference architectures, text classification 1 INTRODUCTION Research in different scientific fields (e.g., natural and social sciences) often require extremely huge computational resources and storage capacity to handle Big Data problems. Traditional sequential data processing algorithms are not sufficient to analyze this large volume of data. For efficient processing and analysis new approaches, techniques and tools are necessary. Moreover, cloud infrastructures and services are becoming even more popular and are nowadays widely used to address the computation and storage requirements of many scientific and commercial Big Data applications. Their widespread usage is a consequence of the dynamic and scalable nature of the services maintained by cloud providers.However, there are several challenges that a data scientist has to face when planning the use or deployment of any Big Data platform on cloud(s). 1The selection of the appropriate cloud provider(s) is always a tiresome process since several factors has to be considered, even when only a generic Infrastructure-as-a-Service (IaaS) provider is required: private (e.g., Agrodat Cloud 2 ), federated (e.g., MTA Cloud 3 or pan-European EGI FedCloud 4 ), or public cloud (e.g., Amazon AWS 5 ).The Hungarian Academy of Sciences (MTA) provides free IaaS cloud (MTA cloud) services for research communities and easy to use, dynamic infrastructures adapted to the actual project requirements. MTA Cloud was established to accelerate research for the scientists of MTA. Nearly 100 projects have been deployed on MTA Cloud since its opening and more and more projects require to use Big Data and machine learning applications.However, the large number of artificial intelligence (AI) tools available for clouds are very complex, and their proper deployment and configuration

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