A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Dynamic Trust Federation in Grids

Grids are becoming economically viable and productive tools. Grids provide a way of utilizing a vast array of linked resources such as computing systems, databases and services online within Virtual Organizations (VO). However, today’s Grid architectures are not capable of supporting dynamic, agile federation across multiple administrative domains and the main barrier, which hinders dynamic federation over short time scales is security. Federating security and trust is one of the most significant architectural issues in Grids. Existing relevant standards and specifications can be used to federate security services, but do not directly address the dynamic extension of business trust relationships into the digital domain. In this paper we describe an experiment in which we highlight those challenging architectural issues and we will further describe how the approach that combines dynamic trust federation and dynamic authorization mechanism can address dynamic security trust federation in Grids. The experiment made with the prototype described in this paper is used in the NextGRID project for the definition of requirements for next generation Grid architectures adapted to business application need

A Generic Deployment Framework for Grid Computing and Distributed Applications

Deployment of distributed applications on large systems, and especially on grid infrastructures, becomes a more and more complex task. Grid users spend a lot of time to prepare, install and configure middleware and application binaries on nodes, and eventually start their applications. The problem is that the deployment process is composed of many heterogeneous tasks that have to be orchestrated in a specific correct order. As a consequence, the automatization of the deployment process is currently very difficult to reach. To address this problem, we propose in this paper a generic deployment framework allowing to automatize the execution of heterogeneous tasks composing the whole deployment process. Our approach is based on a reification as software components of all required deployment mechanisms or existing tools. Grid users only have to describe the configuration to deploy in a simple natural language instead of programming or scripting how the deployment process is executed. As a toy example, this framework is used to deploy CORBA component-based applications and OpenCCM middleware on one thousand nodes of the French Grid5000 infrastructure.Comment: The original publication is available at http://www.springerlink.co

Managing Uncertainty: A Case for Probabilistic Grid Scheduling

The Grid technology is evolving into a global, service-orientated architecture, a universal platform for delivering future high demand computational services. Strong adoption of the Grid and the utility computing concept is leading to an increasing number of Grid installations running a wide range of applications of different size and complexity. In this paper we address the problem of elivering deadline/economy based scheduling in a heterogeneous application environment using statistical properties of job historical executions and its associated meta-data. This approach is motivated by a study of six-month computational load generated by Grid applications in a multi-purpose Grid cluster serving a community of twenty e-Science projects. The observed job statistics, resource utilisation and user behaviour is discussed in the context of management approaches and models most suitable for supporting a probabilistic and autonomous scheduling architecture