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

End-to-end eScience: integrating workflow, query, visualization, and provenance at an ocean observatory

Journal ArticleData analysis tasks at an Ocean Observatory require integrative and and domain-specialized use of database, workflow, visualization systems. We describe a platform to support these tasks developed as part of the cyberinfrastructure at the NSF Science and Technology Center for Coastal Margin Observation and Prediction integrating a provenance-aware workflow system, 3D visualization, and a remote query engine for large-scale ocean circulation models. We show how these disparate tools complement each other and give examples of real scientific insights delivered by the integrated system. We conclude that data management solutions for eScience require this kind of holistic, integrative approach, explain how our approach may be generalized, and recommend a broader, application-oriented research agenda to explore relevant architectures

Computing and data processing

The applications of computers and data processing to astronomy are discussed. Among the topics covered are the emerging national information infrastructure, workstations and supercomputers, supertelescopes, digital astronomy, astrophysics in a numerical laboratory, community software, archiving of ground-based observations, dynamical simulations of complex systems, plasma astrophysics, and the remote control of fourth dimension supercomputers

Future of networking is the future of Big Data, The

2019 Summer.Includes bibliographical references.Scientific domains such as Climate Science, High Energy Particle Physics (HEP), Genomics, Biology, and many others are increasingly moving towards data-oriented workflows where each of these communities generates, stores and uses massive datasets that reach into terabytes and petabytes, and projected soon to reach exabytes. These communities are also increasingly moving towards a global collaborative model where scientists routinely exchange a significant amount of data. The sheer volume of data and associated complexities associated with maintaining, transferring, and using them, continue to push the limits of the current technologies in multiple dimensions - storage, analysis, networking, and security. This thesis tackles the networking aspect of big-data science. Networking is the glue that binds all the components of modern scientific workflows, and these communities are becoming increasingly dependent on high-speed, highly reliable networks. The network, as the common layer across big-science communities, provides an ideal place for implementing common services. Big-science applications also need to work closely with the network to ensure optimal usage of resources, intelligent routing of requests, and data. Finally, as more communities move towards data-intensive, connected workflows - adopting a service model where the network provides some of the common services reduces not only application complexity but also the necessity of duplicate implementations. Named Data Networking (NDN) is a new network architecture whose service model aligns better with the needs of these data-oriented applications. NDN's name based paradigm makes it easier to provide intelligent features at the network layer rather than at the application layer. This thesis shows that NDN can push several standard features to the network. This work is the first attempt to apply NDN in the context of large scientific data; in the process, this thesis touches upon scientific data naming, name discovery, real-world deployment of NDN for scientific data, feasibility studies, and the designs of in-network protocols for big-data science

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Interactive Visualization on High-Resolution Tiled Display Walls with Network Accessible Compute- and Display-Resources

Papers number 2-7 and appendix B and C of this thesis are not available in Munin: 2. Hagen, T-M.S., Johnsen, E.S., Stødle, D., Bjorndalen, J.M. and Anshus, O.: 'Liberating the Desktop', First International Conference on Advances in Computer-Human Interaction (2008), pp 89-94. Available at http://dx.doi.org/10.1109/ACHI.2008.20 3. Tor-Magne Stien Hagen, Oleg Jakobsen, Phuong Hoai Ha, and Otto J. Anshus: 'Comparing the Performance of Multiple Single-Cores versus a Single Multi-Core' (manuscript)4. Tor-Magne Stien Hagen, Phuong Hoai Ha, and Otto J. Anshus: 'Experimental Fault-Tolerant Synchronization for Reliable Computation on Graphics Processors' (manuscript) 5. Tor-Magne Stien Hagen, Daniel Stødle and Otto J. Anshus: 'On-Demand High-Performance Visualization of Spatial Data on High-Resolution Tiled Display Walls', Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications (2010), pages 112-119. Available at http://dx.doi.org/10.5220/0002849601120119 6. Bård Fjukstad, Tor-Magne Stien Hagen, Daniel Stødle, Phuong Hoai Ha, John Markus Bjørndalen and Otto Anshus: 'Interactive Weather Simulation and Visualization on a Display Wall with Many-Core Compute Nodes', Para 2010 – State of the Art in Scientific and Parallel Computing. Available at http://vefir.hi.is/para10/extab/para10-paper-60 7. Tor-Magne Stien Hagen, Daniel Stødle, John Markus Bjørndalen, and Otto Anshus: 'A Step towards Making Local and Remote Desktop Applications Interoperable with High-Resolution Tiled Display Walls', Lecture Notes in Computer Science (2011), Volume 6723/2011, 194-207. Available at http://dx.doi.org/10.1007/978-3-642-21387-8_15The vast volume of scientific data produced today requires tools that can enable scientists to explore large amounts of data to extract meaningful information. One such tool is interactive visualization. The amount of data that can be simultaneously visualized on a computer display is proportional to the display’s resolution. While computer systems in general have seen a remarkable increase in performance the last decades, display resolution has not evolved at the same rate. Increased resolution can be provided by tiling several displays in a grid. A system comprised of multiple displays tiled in such a grid is referred to as a display wall. Display walls provide orders of magnitude more resolution than typical desktop displays, and can provide insight into problems not possible to visualize on desktop displays. However, their distributed and parallel architecture creates several challenges for designing systems that can support interactive visualization. One challenge is compatibility issues with existing software designed for personal desktop computers. Another set of challenges include identifying characteristics of visualization systems that can: (i) Maintain synchronous state and display-output when executed over multiple display nodes; (ii) scale to multiple display nodes without being limited by shared interconnect bottlenecks; (iii) utilize additional computational resources such as desktop computers, clusters and supercomputers for workload distribution; and (iv) use data from local and remote compute- and data-resources with interactive performance. This dissertation presents Network Accessible Compute (NAC) resources and Network Accessible Display (NAD) resources for interactive visualization of data on displays ranging from laptops to high-resolution tiled display walls. A NAD is a display having functionality that enables usage over a network connection. A NAC is a computational resource that can produce content for network accessible displays. A system consisting of NACs and NADs is either push-based (NACs provide NADs with content) or pull-based (NADs request content from NACs). To attack the compatibility challenge, a push-based system was developed. The system enables several simultaneous users to mirror multiple regions from the desktop of their computers (NACs) onto nearby NADs (among others a 22 megapixel display wall) without requiring usage of separate DVI/VGA cables, permanent installation of third party software or opening firewall ports. The system has lower performance than that of a DVI/VGA cable approach, but increases flexibility such as the possibility to share network accessible displays from multiple computers. At a resolution of 800 by 600 pixels, the system can mirror dynamic content between a NAC and a NAD at 38.6 frames per second (FPS). At 1600x1200 pixels, the refresh rate is 12.85 FPS. The bottleneck of the system is frame buffer capturing and encoding/decoding of pixels. These two functional parts are executed in sequence, limiting the usage of additional CPU cores. By pipelining and executing these parts on separate CPU cores, higher frame rates can be expected and by a factor of two in the best case. To attack all presented challenges, a pull-based system, WallScope, was developed. WallScope enables interactive visualization of local and remote data sets on high-resolution tiled display walls. The WallScope architecture comprises a compute-side and a display-side. The compute-side comprises a set of static and dynamic NACs. Static NACs are considered permanent to the system once added. This type of NAC typically has strict underlying security and access policies. Examples of such NACs are clusters, grids and supercomputers. Dynamic NACs are compute resources that can register on-the-fly to become compute nodes in the system. Examples of this type of NAC are laptops and desktop computers. The display-side comprises of a set of NADs and a data set containing data customized for the particular application domain of the NADs. NADs are based on a sort-first rendering approach where a visualization client is executed on each display-node. The state of these visualization clients is provided by a separate state server, enabling central control of load and refresh-rate. Based on the state received from the state server, the visualization clients request content from the data set. The data set is live in that it translates these requests into compute messages and forwards them to available NACs. Results of the computations are returned to the NADs for the final rendering. The live data set is close to the NADs, both in terms of bandwidth and latency, to enable interactive visualization. WallScope can visualize the Earth, gigapixel images, and other data available through the live data set. When visualizing the Earth on a 28-node display wall by combining the Blue Marble data set with the Landsat data set using a set of static NACs, the bottleneck of WallScope is the computation involved in combining the data sets. However, the time used to combine data sets on the NACs decreases by a factor of 23 when going from 1 to 26 compute nodes. The display-side can decode 414.2 megapixels of images per second (19 frames per second) when visualizing the Earth. The decoding process is multi-threaded and higher frame rates are expected using multi-core CPUs. WallScope can rasterize a 350-page PDF document into 550 megapixels of image-tiles and display these image-tiles on a 28-node display wall in 74.66 seconds (PNG) and 20.66 seconds (JPG) using a single quad-core desktop computer as a dynamic NAC. This time is reduced to 4.20 seconds (PNG) and 2.40 seconds (JPG) using 28 quad-core NACs. This shows that the application output from personal desktop computers can be decoupled from the resolution of the local desktop and display for usage on high-resolution tiled display walls. It also shows that the performance can be increased by adding computational resources giving a resulting speedup of 17.77 (PNG) and 8.59 (JPG) using 28 compute nodes. Three principles are formulated based on the concepts and systems researched and developed: (i) Establishing the end-to-end principle through customization, is a principle stating that the setup and interaction between a display-side and a compute-side in a visualization context can be performed by customizing one or both sides; (ii) Personal Computer (PC) – Personal Compute Resource (PCR) duality states that a user’s computer is both a PC and a PCR, implying that desktop applications can be utilized locally using attached interaction devices and display(s), or remotely by other visualization systems for domain specific production of data based on a user’s personal desktop install; and (iii) domain specific best-effort synchronization stating that for distributed visualization systems running on tiled display walls, state handling can be performed using a best-effort synchronization approach, where visualization clients eventually will get the correct state after a given period of time. Compared to state-of-the-art systems presented in the literature, the contributions of this dissertation enable utilization of a broader range of compute resources from a display wall, while at the same time providing better control over where to provide functionality and where to distribute workload between compute-nodes and display-nodes in a visualization context

Adaptive remote visualization system with optimized network performance for large scale scientific data

This dissertation discusses algorithmic and implementation aspects of an automatically configurable remote visualization system, which optimally decomposes and adaptively maps the visualization pipeline to a wide-area network. The first node typically serves as a data server that generates or stores raw data sets and a remote client resides on the last node equipped with a display device ranging from a personal desktop to a powerwall. Intermediate nodes can be located anywhere on the network and often include workstations, clusters, or custom rendering engines. We employ a regression model-based network daemon to estimate the effective bandwidth and minimal delay of a transport path using active traffic measurement. Data processing time is predicted for various visualization algorithms using block partition and statistical technique. Based on the link measurements, node characteristics, and module properties, we strategically organize visualization pipeline modules such as filtering, geometry generation, rendering, and display into groups, and dynamically assign them to appropriate network nodes to achieve minimal total delay for post-processing or maximal frame rate for streaming applications. We propose polynomial-time algorithms using the dynamic programming method to compute the optimal solutions for the problems of pipeline decomposition and network mapping under different constraints. A parallel based remote visualization system, which comprises a logical group of autonomous nodes that cooperate to enable sharing, selection, and aggregation of various types of resources distributed over a network, is implemented and deployed at geographically distributed nodes for experimental testing. Our system is capable of handling a complete spectrum of remote visualization tasks expertly including post processing, computational steering and wireless sensor network monitoring. Visualization functionalities such as isosurface, ray casting, streamline, linear integral convolution (LIC) are supported in our system. The proposed decomposition and mapping scheme is generic and can be applied to other network-oriented computation applications whose computing components form a linear arrangement

Introducing distributed dynamic data-intensive (D3) science: Understanding applications and infrastructure

A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Data sets are growing larger and becoming distributed; and their location, availability and properties are often time-dependent. Collectively, these characteristics give rise to dynamic distributed data-intensive applications. While "static" data applications have received significant attention, the characteristics, requirements, and software systems for the analysis of large volumes of dynamic, distributed data, and data-intensive applications have received relatively less attention. This paper surveys several representative dynamic distributed data-intensive application scenarios, provides a common conceptual framework to understand them, and examines the infrastructure used in support of applications.Comment: 38 pages, 2 figure