Grid Computing: The Next Decade--Report and Summary

2012

The adoption of open standards-based interoperable grid and cloud computing infrastructure is a way to support research in Europe that can be empowered through ubiquitous, trusted, and easy trans-national access to services for data, computation, communication and collaborative work. SIENA is the European Commission-funded project whose roadmap assesses the situation, identifies issues and makes recommendations on the adoption and evolution of grid and cloud standards for e-science and beyond. These recommendations look to continue the dialogue between public sector officials, industry and Standard Development Organizations over the deployment of cloud based services for the scientific community and public procurement. This paper presents the results of the SIENA roadmap together with its recommendations that are focused on the work of Europe's Distributed Computing Infrastructure initiatives in advancing the standards landscape, especially with regard to the adoption of cloud standards such as OCCI, CDM and OVF.

Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07), 2007

The Computer Science discipline, especially in large scale distributed systems like Grids and P2P systems and in high performance computing areas, tends to address issues related to increasingly complex systems, gathering thousands to millions of non trivial components. Theoretical analysis, simulation and even emulation are reaching their limits. Like in other scientific disciplines such as physics, chemistry and life science, there is a need to develop, run and maintain generations of scientific instruments for the observation of complex distributed systems running at real scale and under reproducible experimental conditions. Grid'5000 and DAS3 are two large scale systems designed as scientific instruments for researchers in the domains of Grid, P2P and networking. More than testbeds, Grid'5000 and DAS3 have been designed as "Computer Science Grids", where researchers share experimental resources spanning over large geographical distances, are able to reserve resources, configure them, run their experiments, realize precise measurements and replay the same experiments with the same experimental conditions. Computer scientists use these two platforms to address issues in the different software layers between the hardware and the users: networking protocols, OS, middleware, parallel and distributed application runtimes, and applications. In this paper, we will present two Computer Science Grids: Grid'5000 and DAS3. We will describe the motivations, design and current status of these two systems. We will also present some of their key results, not only in terms of scientific results in computer science, but also the impact of these two systems as research tools. The success of the Grid'5000 and DAS platforms is the basis of an international initiative, having the objective to deploy a European level "Computer Science Grid".

2006

This workshop will focus on projects and technologies that are adopting scientific portals and gateways. These technologies are characterized by delivering well-established mechanisms for providing familiar interfaces to secure Grid resources, services, applications, tools, and collaboration services for communities of scientists. In most cases access is enabled through a web browser without the need to download or install any specialized software or worry about networks and ports. As a result, the science application user is isolated from the complex details and infrastructure needed to operate an application on the Grid.

Cluster Computing, 2002

Computational Grids [17, 25] have become an important asset in large-scale scientific and engineering research. By providing a set of services that allow a widely distributed collection of resources to be tied together into a relatively seamless computing framework, teams of researchers can collaborate to solve problems that they could not have attempted before. Unfortunately the task of building Grid applications remains extremely difficult because there are few tools available to support developers. To build reliable and re-usable Grid ...

Proceedings of the …, 2000

We use the term "Grid" to refer to a software system that provides uniform and location independent access to geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. While, in general, Grids will provide the infrastructure to support a wide range of services in the scientific environment (e.g. collaboration and remote instrument control) in this paper we focus on services for high performance computing and data handling. We describe the services and architecture of NASA's Information Power Grid ("IPG") -an early example of a large-scale Grid -and some of the issues that have come up in its implementation.

2005

Grid Computing, 2003

Grid Computing. Wiley InterScience, Hoboken, NJ, S, 2003

Part A of this book, chapters 1 to 5, provides an overview and motivation for Grids. Further chapter 37 is an illuminating discussion from 1992 of Metacomputing-a key early concept on which much of the Grid has been built. Chapter 2 is a short overview of the Grid reprinted from Physics Today [14]. Chapter 3 gives a detailed recent history of the Grid while chapter 4 describes the software environment of the seminal I-WAY experiment at SC95. This conference project challenged participantsincluding for instance the Legion activity of chapter 10-to demonstrate Grid-like applications on an OC-3 backbone. Globus [6] grew out of the software needed to support these 60 applications at 17 sites; the human intensive scheduling and security used by I-WAY showed the way to today's powerful approaches. Many of these applications employed visualization including CAVE virtual reality stations as demonstrated in the early Metacomputing work of chapter 37. Chapter 5 brings us to 2002 and describes the experience building Globus-based grids for NASA and DoE. Metacomputing and hence the Grid was born in the High Performance Computing and Communication (HPCC) activities of the 1980's and 1990's. In particular the multiagency Grand (application) Challenges brought critical issues to the fore. We realized the importance of coupling programs together to solve multidisciplinary applications. Here we see beginnings of "Science as a team sport" elaborated in section 9. We saw applications like that of figure 2 that linked instruments, visualization, computing and data [3]-often in a pipeline-and were one of the first important classes of successful Grid applications. HPCC of course built on the growing use of parallel computing and software infrastructure like PVM, MPI, HPF, and OpenMP to support scalable applications. Initially it was thought that the Grid would be most useful in extending these parallel computing approaches from tightly coupled clusters to geographically distributed systems. However more important has been the integration of loosely coupled system-each component of which might be running in parallel on a low-latency parallel machine. The critical Grid task of managing these heterogeneous components as we scale the size of distributed systems replaces that of the tight synchronization of the typically identical (in program but not data as in SPMD-single program multiple data-model) parts of a domain decomposed parallel application. Networking was injected into the Grid-initially with initiatives like the Gigabit testbed program [15] with its projects-Aurora, Blanca, Casa, Nectar, and Vistanet-and dual goals: to investigate potential testbed network architectures, and to explore their usefulness for end-users. Computational Grids got their name from analogies with other national or global infrastructure systems [16]. They share properties with Electric Power Grids; both are ubiquitous; in both cases one does not need to know source of (electric or computing) power (transformer or generator, PC or supercomputer) and the supplying organization. Computational Grids also have differences-they have a wider performance spectrum, more and more heterogeneous services, complex access and security issues, and complex socio-political factors. As we move from distributed to Grid computing, we are moving from largely addressing geographical separation to a focus on the integration and management of software. We are developing a new software engineering as the Grid Web services described starting in chapter 7, provide a component model applicable to any software development project. Interesting features include intrinsic self-documentation of such

WSEAS Transactions on Computers, 2010

During last years the e-Infrastructure concept has been extensively elaborated and promoted as an essential pillar for the implementation of ERA (European Research Area). The main components of this infrastructure are the network for education and research and the Grid infrastructures. At the European level important efforts and resourced have been devoted to the development of both components, with joint financial support provided by Framework Programmes 6 and 7 of EU and national research programmes. At the national level, the high-speed communication network for education and research, and national grid infrastructure for research are included in the list of priority investment projects in the Information and Communications Technologies domain. The main financial support for the development of these projects is provided by the Sectorial Operational Programme "Increase of Economic Competitiveness". ICI Bucharest is among beneficiary organizations of this support. This paper presents the European context for e-Infrastructure development as support for e-Science concept implementation, the evolution and current status of Grid infrastructures at the European and national levels, a short review of the development of Grid computing at ICI Bucharest, the main objectives and activities of the structural funds grant awarded to the institute for a significant upgrade of the current grid site RO-01-ICI.

Doctoral Thesis, 2006

The practice of science experienced a number of paradigm shifts in the 20th century, including the growth of large geographically dispersed teams and the use of simulations and computational science as a third branch, complementing theory and laboratory experiments. The recent exponential growth in network capacity, brought about by the rapid development of agile optical transport, is resulting in another such shift as the 21st century progresses. Essential to this new branch of e-Science applications is the capability of transferring immense amounts of data: dozens and hundreds of TeraBytes and even PetaBytes. The invention of the transistor in 1947 at Bell Labs was the triggering event that led to the technology revolution of the 20th century. The completion of the Human Genome Project (HGP) in 2003 was the triggering event for the life science revolution of the 21st century. The understanding of the genome, DNA, proteins, and enzymes is prerequisite to modifying their properties and the advancement of systematic biology. Grid Computing has become the fundamental platform to conduct this e-Science research. Vast increases in data generation by e-Science applications, along with advances in computation, storage and communication, affect the nature of scientific research. During this decade, crossing the "Peta" line is expected: Petabyte in data size, Petaflop in CPU processing, and Petabit/s in network bandwidth. Numerous challenges arise from a network with a capacity millions of times greater than the public Internet. Currently, the distribution of large amounts of data is restricted by the inherent bottleneck nature of today"s public Internet architecture, which employs packet switching technologies. Bandwidth limitations of the Internet inhibit the advancement and utilization of new e-Science applications in Grid Computing. These emerging e-Science applications are evolving in data centers and clusters; however, the potential capability of a globally distributed system over long distances is yet to be realized. Today's network orchestration of resources and services is done manually via multi-party conference calls, emails, yellow sticky notes, and reminder communications, all of which rely on human interaction to get results. The work in this thesis automates the orchestration of networks with other resources, better utilizing all resources in a time efficient manner. Automation allows for a vastly more comprehensive use of all components and removes human limitations from the process. We demonstrated automatic Lambda setting-up and tearing-down as part of application servers over MEMs testbed in Chicago metro area in a matter of seconds; and across domains, over transatlantic links in around minute. The main goal of this thesis is to build a new grid-computing paradigm that fully harnesses the available communication infrastructure. An optical network functions as the third leg in orchestration with computation and storage. This tripod architecture becomes the foundation of global distribution of vast amounts of data in emerging e-Science applications. A key investigation area of this thesis is the fundamental technologies that allow e-Science applications in Grid Virtual Organization (VO) to access abundant optical bandwidth through the new technology of Lambda on demand. This technology provides essential networking fundamentals that are presently missing from the Grid Computing environment. Further, this technology overcomes current bandwidth limitations, making VO a reality and consequentially removing some basic limitations to the growth of this new big science branch. In this thesis, the Lambda Data Grid provides the knowledge plane that allows e-Science applications to transfer enormous amounts of data over a dedicated Lightpath, resulting in the true viability of global VO. This enhances science research by allowing large distributed teams to work efficiently, utilizing simulations and computational science as a third branch of research.