Sharing digital object across data infrastructures using Named Data Networking (NDN)

Data infrastructures manage the life cycle of digital assets and allow users to efficiently discover them. To improve the Findability, Accessibility, Interoperability and Re-usability (FAIRness) of digital assets, a data infrastructure needs to provide digital assets with not only rich meta information and semantics contexts information but also globally resolvable identifiers. The Persistent Identifiers (PIDs), like Digital Object Identifier (DOI) are often used by data publishers and infrastructures. The traditional IP network and client-server model can potentially cause congestion and delays when many consumers simultaneously access data. In contrast, Information-Centric Networking (ICN) technologies such as Named Data Networking (NDN) adopt a data-centric approach where digital data objects, once requested, may be stored on intermediate hops in the network. Consecutive requests for that unique digital object are then made available by these intermediate hops (caching). This approach distributes traffic load more efficient and reliable compared to host-to-host connection-oriented techniques and demonstrates attractive opportunities for sharing digital objects across distributed networks. However, such an approach also faces several challenges. It requires not only an effective translation between the different naming schemas among PIDs and NDN, in particular for supporting PIDs from different publishers or repositories. Moreover, the planning and configuration of an ICN environment for distributed infrastructures are lacking an automated solution. To bridge the gap, we propose an ICN planning service with specific consideration of interoperability across PID schemas in the Cloud environment

Using SDN as a Technology Enabler for Distance Learning Applications

The number of students who obtained degrees via distance learning has grown considerably in the last few years. Services provided by distance learning systems are expected to be delivered in a fast and reliable way. However, as the number of users increases, so does the stress on the network. Software-Defined Networking, on the other hand, is a new technology that provides a rapid response to the ever-evolving requirements of today’s businesses. The technology is expected to enhance the overall performance of cloud services, including those provided by distance learning. This paper investigates the benefits of employing such a technology by educational institutions to provide quality services to the users. The results of the experiments show an improvement in performance of up to 11%, when utilizing the technology. In addition, we show how resource reservation features can be utilized to provide quality service to users depending on their role in the distance learning system

PROCESS Data Infrastructure and Data Services

Due to energy limitation and high operational costs, it is likely that exascale computing will not be achieved by one or two datacentres but will require many more. A simple calculation, which aggregates the computation power of the 2017 Top500 supercomputers, can only reach 418 petaflops. Companies like Rescale, which claims 1.4 exaflops of peak computing power, describes its infrastructure as composed of 8 million servers spread across 30 datacentres. Any proposed solution to address exascale computing challenges has to take into consideration these facts and by design should aim to support the use of geographically distributed and likely independent datacentres. It should also consider, whenever possible, the co-allocation of the storage with the computation as it would take 3 years to transfer 1 exabyte on a dedicated 100 Gb Ethernet connection. This means we have to be smart about managing data more and more geographically dispersed and spread across different administrative domains. As the natural settings of the PROCESS project is to operate within the European Research Infrastructure and serve the European research communities facing exascale challenges, it is important that PROCESS architecture and solutions are well positioned within the European computing and data management landscape namely PRACE, EGI, and EUDAT. In this paper we propose a scalable and programmable data infrastructure that is easy to deploy and can be tuned to support various data-intensive scientific applications

Time critical requirements and technical considerations for advanced support environments for data-intensive research

Data-centric approaches play an increasing role in many scientific domains, but in turn rely increasingly heavily on advanced research support environments for coordinating research activities, providing access to research data, and choreographing complex experiments. Critical time constraints can be seen in several application scenarios e.g., event detection for disaster early warning, runtime execution steering, and failure recovery. Providing support for executing such time critical research applications is still a challenging issue in many current research support environments however. In this paper, we analyse time critical requirements in three key kinds of research support environment—Virtual Research Environments, Research Infrastructures, and e-Infrastructures—and review the current state of the art. An approach for dynamic infrastructure planning is discussed that may help to address some of these requirements. The work is based on requirements collection recently performed in three EU H2020 projects: SWITCH, ENVRIPLUS and VRE4EIC

Towards Interoperable Research Infrastructures for Environmental and Earth Sciences

This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions