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Institute: Institute of Informatics

Integrating and managing services for the European Open Science Cloud (EOSC-hub)
Integrovanie a manažment služieb pre európsky cloud pre otvorenú vedu
Program: Horizon 2020
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:The EOSC-hub project creates the integration and management system of the future European Open Science Cloud that delivers a catalogue of services, software and data from the EGI Federation, EUDAT CDI, INDIGO-DataCloud and major research e-infrastructures. This integration and management system (the Hub) builds on mature processes, policies and tools from the leading European federated e-Infrastructures to cover the whole life-cycle of services, from planning to delivery. The Hub aggregates services from local, regional and national e-Infrastructures in Europe, Africa, Asia, Canada and South America. The Hub acts as a single contact point for researchers and innovators to discover, access, use and reuse a broad spectrum of resources for advanced data-driven research. Through the virtual access mechanism, more scientific communities and users have access to services supporting their scientific discovery and collaboration across disciplinary and geographical boundaries. The project also improves skills and knowledge among researchers and service operators by delivering specialised trainings and by establishing competence centres to co-create solutions with the users. In the area of engagement with the private sector, the project creates a Joint Digital Innovation Hub that stimulates an ecosystem of industry/SMEs, service providers and researchers to support business pilots, market take-up and commercial boost strategies. EOSC-hub builds on existing technology already at TRL 8 and addresses the need for interoperability by promoting the adoption of open standards and protocols. By mobilizing e-Infrastructures comprising more than 300 data centres worldwide and 18 pan-European infrastructures, this project is a ground-breaking milestone for the implementation of the European Open Science Cloud.
Duration: 1.1.2018 - 31.12.2020

Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud (DEEP-HybridDataCloud)
Návrh a sprístupnenie e-infraštruktúr pre intenzívne spracovanie v hybridnom dátovom cloude
Program: Horizon 2020
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:The key concept proposed in the DEEP Hybrid DataCloud project is the need to support intensive computing techniques that require specialized HPC hardware, like GPUs or low latency interconnects, to explore very large datasets. A Hybrid Cloud approach enables the access to such resources that are not easily reachable by the researchers at the scale needed in the current EU e-infrastructure. We also propose to deploy under the common label of “DEEP as a Service” a set of building blocks that enable the easy development of applications requiring these techniques: deep learning using neural networks, parallel post-processing of very large data, and analysis of massive online data streams. Three pilot applications exploiting very large datasets in Biology, Physics and Network Security are proposed, and further pilots for dissemination into other areas like Medicine, Earth Observation, Astrophysics, and Citizen Science will be supported in a testbed with significant HPC resources, including latest generation GPUs, to evaluate the performance and scalability of the solutions. A DevOps approach will be implemented to provide the chain to ensure the quality of the software and services released, that will also be offered to the developers of research applications. The project will evolve to TRL8 existing services and technologies at TRL6+, including relevant contributions to the EOSC by the INDIGO-DataCloud H2020 project, that the project will enrich with new functionalities already available as prototypes, notably the support for GPUs and low latency interconnects. These services will be deployed in the project testbed, offered to the research communities linked to the project through pilot applications, and integrated under the EOSC framework, where they can be further scaled up in the future.
Duration: 1.11.2017 - 30.4.2020

Development of software tools for analysis and synthesis of schedulers for cloud computing
Návrh softvérových nástrojov pre analýzu a syntézu plánovačov pre počítanie v cloude
Program: Inter-academic agreement
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:Modern scientific problems require significant computing resources, so the problem of resources optimization in multiprocessor environments is very important. We will focus on cloud systems as one of the most recent and promising fields of high-performance computing. Cloud computing systems operate in complex heterogeneous environments. It is common to have a single physical server with many simultaneous programs from different users competing for computing and network resources. In most cases, especially for public cloud, the user is unable to control the distribution of resources. The allocation algorithms may contain defects and inefficiency and this can lead to a significant increase in processing time, that is why cloud computing require efficient algorithms providing flexible and stable allocation of resources. The problem is in unfair and uneven access to resources, caused by heterogeneity of users and their tasks where each user is rational agent that tries to increase its share of resources. This could bring the system to the inefficient equilibrium., so a key element of cloud systems are efficient algorithms for load distribution – schedulers and brokers, providing services to users. The idea of this project is to apply game-theoretic approach to the problem of scheduling and allocation of computing resources in dynamic heterogeneous environment with many competitive users and provide software tools based on game-theoretic construction. Another idea of the project is an optimization approach that respects the requirements of end-users. This task will be modelled as a multi-criteria optimization problem. Each objective of the optimization will be associated with a weight. The weight will express the priority of the optimization objective. This way of the optimization will enable the end-users to adjust it to their needs. The problem will be sorted out in a general way so the solution will support various cloud providers. The project continues the previous cooperation of our Institute in constructing adaptive methods for programming high-performance computing on heterogeneous multiprocessor systems.
Duration: 6.4.2017 - 31.12.2019

PROviding Computing solutions for ExaScale ChallengeS (PROCESS)
Poskytovanie výpočtových riešení pre výzvy v oblasti ExaScale
Program: Horizon 2020
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:The PROCESS demonstrators will pave the way towards exascale data services that will accelerate innovation and maximise the benefits of these emerging data solutions. The main tangible outputs of PROCESS are five very large data service prototypes, implemented using a mature, modular, generalizable open source solution for user friendly exascale data. The services will be thoroughly validated in real-world settings, both in scientific research and in industry pilot deployments. To achieve these ambitious objectives, the project consortium brings together the key players in the new data-driven ecosystem: top-level HPC and big data centres, communities – such as Square Kilometre Array (SKA) project – with unique data challenges that the current solutions are unable to meet and experienced e-Infrastructure solution providers with an extensive track record of rapid application development. In addition to providing the service prototypes that can cope with very large data, PROCESS addresses the work programme goals by using the tools and services with heterogeneous use cases, including medical informatics, airline revenue management and open data for global disaster risk reduction. This diversity of user communities ensures that in addition to supporting communities that push the envelope, the solutions will also ease the learning curve for broadest possible range of user communities. In addition, the chosen open source strategy maximises the potential for uptake and reuse, together with mature software engineering practices that minimise the efforts needed to set up and maintain services based on the PROCESS software releases.
Duration: 1.11.2017 - 30.10.2020

Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice
Zvýšenie aplikovateľnosti prírodou inšpirovaných optimalizačných metód prepájaním teórie a praxe.
Program: COST
Project leader: Ing. Budinská Ivana PhD.
Annotation:The main objective of the COST Action is to bridge this gap and improve the applicability of all kinds of nature-inspired optimisation methods. It aims at making theoretical insights more accessible and practical by creating a platform where theoreticians and practitioners can meet and exchange insights, ideas and needs; by developing robust guidelines and practical support for application development based on theoretical insights; by developing theoretical frameworks driven by actual needs arising from practical applications; by training Early Career Investigators in a theory of nature-inspired optimisation methods that clearly aims at practical applications; by broadening participation in the ongoing research of how to develop and apply robust nature-inspired optimisation methods in different application areas.
Duration: 9.3.2016 - 8.3.2020

The total number of projects: 5