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

The Virginia Tech Computational Grid: A Research Agenda

An important goal of grid computing is to apply the rapidly expanding power of distributed computing resources to large-scale multidisciplinary scientic problem solving. Developing a usable computational grid for Virginia Tech is desirable from many perspectives. It leverages distinctive strengths of the university, can help meet the research computing needs of users with the highest demands, and will generate many challenging computer science research questions. By deploying a campus-wide grid and demonstrating its effectiveness for real applications, the Grid Computing Research Group hopes to gain valuable experience and contribute to the grid computing community. This report describes the needs and advantages which characterize the Virginia Tech context with respect to grid computing, and summarizes several current research projects which will meet those needs

Survey and Analysis of Production Distributed Computing Infrastructures

This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures

INDIGO-Datacloud: foundations and architectural description of a Platform as a Service oriented to scientific computing

Software Engineering.-- et al.In this paper we describe the architecture of a Platform as a Service (PaaS) oriented to computing and data analysis. In order to clarify the choices we made, we explain the features using practical examples, applied to several known usage patterns in the area of HEP computing. The proposed architecture is devised to provide researchers with a unified view of distributed computing infrastructures, focusing in facilitating seamless access. In this respect the Platform is able to profit from the most recent developments for computing and processing large amounts of data, and to exploit current storage and preservation technologies, with the appropriate mechanisms to ensure security and privacy.INDIGO-DataCloud is co-founded by the Horizon 2020Framework Programme.Peer reviewe

Efficient Communication and Coordination for Large-Scale Multi-Agent Systems

The growth of the computational power of computers and the speed of networks has made large-scale multi-agent systems a promising technology. As the number of agents in a single application approaches thousands or millions, distributed computing has become a general paradigm in large-scale multi-agent systems to take the benefits of parallel computing. However, since these numerous agents are located on distributed computers and interact intensively with each other to achieve common goals, the agent communication cost significantly affects the performance of applications. Therefore, optimizing the agent communication cost on distributed systems could considerably reduce the runtime of multi-agent applications. Furthermore, because static multi-agent frameworks may not be suitable for all kinds of applications, and the communication patterns of agents may change during execution, multi-agent frameworks should adapt their services to support applications differently according to their dynamic characteristics. This thesis proposes three adaptive services at the agent framework level to reduce the agent communication and coordination cost of large-scale multi-agent applications. First, communication locality-aware agent distribution aims at minimizing inter-node communication by collocating heavily communicating agents on the same platform and maintaining agent group-based load sharing. Second, application agent-oriented middle agent services attempt to optimize agent interaction through middle agents by executing application agent-supported search algorithms on the middle agent address space. Third, message passing for mobile agents aims at reducing the time of message delivery to mobile agents using location caches or by extending the agent address scheme with location information. With these services, we have achieved very impressive experimental results in large- scale UAV simulations including up to 10,000 agents. Also, we have provided a formal definition of our framework and services with operational semantics

A Grid-Enabled Infrastructure for Resource Sharing, E-Learning, Searching and Distributed Repository Among Universities

In the recent years, service-based approaches for sharing of data among repositories and online learning are rising to prominence because of their potential to meet the requirements in the area of high performance computing. Developing education based grid services and assuring high availability reliability and scalability are demanding in web service architectures. On the other hand, grid computing provides flexibility towards aggregating distributed CPU, memory, storage, data and supports large number of distributed resource sharing to provide the full potential for education like applications to share the knowledge that can be attainable on any single system. However, the literature shows that the potential of grid resources for educational purposes is not being utilized yet. In this paper, an education based grid framework architecture that provides promising platform to support sharing of geographically dispersed learning content among universities is developed. It allows students, faculty and researchers to share and gain knowledge in their area of interest by using e-learning, searching and distributed repository services among universities from anywhere, anytime. Globus toolkit 5.2.5 (GTK) software is used as grid middleware that provides resource access, discovery and management, data movement, security, and so forth. Furthermore, this work uses the OGSA-DAI that provides database access and operations. The resulting infrastructure enables users to discover education services and interact with them using the grid portal

Overview and Evaluation of Conceptual Strategies for Accessing CPU-Dependent Execution Resources in Grid Infrastructures

The emergence of many-core and massively-parallel computational accelerators (e.g., GPGPUs) has led to user demand for such resources in grid infrastructures. A widely adopted approach for discovering and accessing such resources has, however, yet to emerge.  GPGPUs are an example of a larger class of computational resources, characterized in part by dependence on an allocated CPU. This paper terms such resources "CPU-Dependent Execution Resources" (CDERs). Five conceptual strategies for discovering and accessing CDERs are described and evaluated against key criteria, and all five strategies are compliant with GLUE 1.3, GLUE 2.0, or both. From this evaluation, two of the presented strategies clearly emerge as providing the greatest flexibility for publishing both static and dynamic CDER information and identifying CDERs that satisfy specific job requirements. Furthermore, a two-phase approach to job-submission is proposed for those jobs requiring access to CDERs. The approach is compatible with existing grid services.  Examples are provided to illustrate job submission under each strategy