Agency: Cordis | Branch: FP7 | Program: MC-ITN | Phase: FP7-PEOPLE-ITN-2008 | Award Amount: 3.30M | Year: 2009
Storage research increasingly gains importance based on the tremendous need for storage capacity and I/O performance. Over the past years, several trends have considerably changed the design of storage systems, starting from new storage media over the widespread use of storage area networks, up to grid and cloud storage concepts. Furthermore, to achieve cost efficiency, storage systems are increasingly assembled from commodity components. Thus, we are in the middle of an evolution towards a new storage architecture made of many decentralized commodity components with increased processing and communication capabilities, which requires the introduction of new concepts to benefit from the resulting architectural opportunities. The consortium of this Marie Curie Initial Training Network (MCITN) SCALing by means of Ubiquitous Storage (SCALUS) aims at elevating education, research, and development inside this exciting area with a focus on cluster, grid, and cloud storage. The vision of this MCITN is to deliver the foundation for ubiquitous storage systems, which can be scaled in arbitrary directions (capacity, performance, distance, security) Providing ubiquitous storage will become a major demand for future IT systems and leadership in this area can have significant impact on European competitiveness in IT technology. To get this leadership, it is necessary to invest into storage education and research and to bridge the current gap between local storage, cluster storage, grid storage, and cloud storage. The consortium will proceed into the direction by building the first interdisciplinary teaching and research network on storage issues. It consists of top European institutes and companies in storage and cluster technology, building a demanding but rewarding interdisciplinary environment for young researchers.
Agency: Cordis | Branch: FP7 | Program: CP-FP | Phase: NMP-2008-3.3-1 | Award Amount: 4.56M | Year: 2009
Non-hierarchical production networks are a common environment of todays manufacturing companies. Each company is facing multiple and dynamic relationships within several networks. This complex situation causes permanent delivery delays. Delayed supplies lead to wasteful turbulences in the entire network and to expensive compensations. For the European manufacturing industry the loss of efficiency is estimated to be 1 billion Euros per year. Besides the costs the missing delivery reliability leads to poor customer satisfaction and increased lead times. The key objective of inTime is to improve delivery reliability in each customer-supplier relationship balancing production in the overall network. In order to foster delivery reliability each supply has to be rewarded according to his delivery performance. Today only a minority of supplies is successfully secured by manual negotiated delivery penalties. Due to the high variation of supplies manual levelling of incentives is a time consuming process. Therefore a fair share of risk between customer and supplier is not transparent. inTime will reach transparency by developing a market based negotiation mechanism rewarding delivery reliability at minimum transaction costs. Key innovation of the project is to establish an electronic market for trading reliability incentives. Besides the development of the market mechanism, the integration into the entire order process requires several novelties which have to be developed simultaneously: - The negotiation mechanism supports a new kind of order prioritisation. To realise this potential new planning and sequencing algorithms are needed. - In order to gain transparency on the criticality of each supply, functions analysing the internal risk and related costs of the required parts have to be developed. - Enabler for inTime is an automated order data communication. To avoid redundant developments inTime will apply the existing communication platform myOpenFactory.
Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2013.1.1 | Award Amount: 4.55M | Year: 2014
Nowadays, while most of the programmable network apparatus vendors support OpenFlow, a number of fragmented control plane solutions exist for proprietary software-defined networks. Thus, network applications developers need to re-code their solutions every time they encounter a network infrastructure based on a different controller. Moreover, different network developers adopt different solutions as abstract control plane programming language (e.g. Frenetic, Procera), leading to not reusable and shareable source code for network programs.So despite having OpenFlow as the candidate for a standard interface between the controller and the network infrastructure, interworking between different controllers and network devices is hindered and walled gardens are emerging. NetIDE will deliver a single integrated development environment to support the whole development lifecycle of network controller programs in a vendor-independent fashion.NetIDE will approach the problem by proposing an architecture that will allow the different representation to be used to program the network and different controllers to execute the network programs. In this respect the core work will be definition of a common language able to cover different network programming styles: the NetIDE IRF. Around IRF we will explore fundamental research topics, such as: development of controller agnostic Network Apps (applications that control network behavior) and Network Services (services that support the task of network controllers); cross-controller debugging and profiling of network programs; heterogeneous network programming; network programming with simulators in the loop. NetIDE IRF will be supported by a developer toolkit to allow creation of Network Apps and by a Network App Engine supporting the execution and testing of NetIDE IRF based applications. NetIDE will result in one-stop solution for the development of SDN applications that covers all the development lifecycle.
Agency: Cordis | Branch: H2020 | Program: MSCA-ITN-ETN | Phase: MSCA-ITN-2014-ETN | Award Amount: 3.80M | Year: 2015
The consortium of this European Training Network (ETN) BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data will train future data scientists in order to enable them and us to apply holistic and interdisciplinary approaches for taking advantage of a data-overwhelmed world, which requires HPC and Cloud infrastructures with a redefinition of storage architectures underpinning them - focusing on meeting highly ambitious performance and energy usage objectives. There has been an explosion of digital data, which is changing our knowledge about the world. This huge data collection, which cannot be managed by current data management systems, is known as Big Data. Techniques to address it are gradually combining with what has been traditionally known as High Performance Computing. Therefore, this ETN will focus on the convergence of Big Data, HPC, and Cloud data storage, ist management and analysis. To gain value from Big Data it must be addressed from many different angles: (i) applications, which can exploit this data, (ii) middleware, operating in the cloud and HPC environments, and (iii) infrastructure, which provides the Storage, and Computing capable of handling it. Big Data can only be effectively exploited if techniques and algorithms are available, which help to understand its content, so that it can be processed by decision-making models. This is the main goal of Data Science. We claim that this ETN project will be the ideal means to educate new researchers on the different facets of Data Science (across storage hardware and software architectures, large-scale distributed systems, data management services, data analysis, machine learning, decision making). Such a multifaceted expertise is mandatory to enable researchers to propose appropriate answers to applications requirements, while leveraging advanced data storage solutions unifying cloud and HPC storage facilities.
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: FETHPC-1-2014 | Award Amount: 8.11M | Year: 2015
The overall objective of the Next Generation I/O Project (NEXTGenIO) is to design and prototype a new, scalable, high-performance, energy efficient computing platform designed to address the challenge of delivering scalable I/O performance to applications at the Exascale. It will achieve this using highly innovative, non-volatile, dual in-line memory modules (NV-DIMMs). These hardware and systemware developments will be coupled to a co-design approach driven by the needs of some of todays most demanding HPC applications. By meeting this overall objective, NEXTGenIO will solve a key part of the Exascale challenge and enable HPC and Big Data applications to overcome the limitations of todays HPC I/O subsystems. Today most high-end HPC systems employ data storage separate from the main system and the I/O subsystem often struggles to deal with the degree of parallelism present. As we move into the domain of extreme parallelism at the Exascale we need to address I/O if such systems are to deliver appropriate performance and efficiency for their application user communities. The NEXTGenIO project will explore the use of NV-DIMMs and associated systemware developments through a co-design process with three end-user partners: a high-end academic HPC service provider, a numerical weather forecasting service provider and a commercial on-demand HPC service provider. These partners will develop a set of I/O workload simulators to allow quantitative improvements in I/O performance to be directly measured on the new system in a variety of research configurations. Systemware software developed in the project will include performance analysis tools, improved job schedulers that take into account data locality and energy efficiency, optimised programming models, and APIs and drivers for optimal use of the new I/O hierarchy. The project will deliver immediately exploitable hardware and software results and show how to deliver high performance I/O at the Exascale.