Tobias Scholl

Grid-Based Data Stream Processing in e-Science

The field of e-science currently faces many challenges. Among the most important ones are the analysis of huge volumes of scientific data and the connection of various sciences and communities, thus enabling scientists to share scientific interests, data, and research results. These issues can be addressed by processing large data volumes on-thefly in the form of data streams and by combining multiple data sources and making the results available in a network. In this paper, we demonstrate how e-science can benefit from research in computer science in the field of data stream management. In particular, we are concerned with processing multiple data streams in grid-based peer-to-peer (P2P) networks. We introduce spatial matching, which is a current issue in astrophysics, as a real-life e-science scenario to show how a data stream management system (DSMS) can help in efficiently performing associated tasks. We describe our new way of solving the spatial matching problem and present some evaluation results. In the course of the evaluation, our DSMS StarGlobe proves to be a valuable computing platform for astrophysical applications.

Scalable community-driven data sharing in e-science grids

E-science projects of various disciplines face a fundamental challenge: thousands of users want to obtain new scientific results by application-specific and dynamic correlation of data from globally distributed sources. Considering the involved enormous and exponentially growing data volumes, centralized data management reaches its limits. Since scientific data are often highly skewed and exploration tasks exhibit a large degree of spatial locality, we propose the locality-aware allocation of data objects onto a distributed network of interoperating databases. HiSbase is an approach to data management in scientific federated Data Grids that addresses the scalability issue by combining established techniques of database research in the field of spatial data structures (quadtrees), histograms, and parallel databases with the scalable resource sharing and load balancing capabilities of decentralized Peer-to-Peer (P2P) networks. The proposed combination constitutes a complementary e-science infrastructure enabling load balancing and increased query throughput.

Community Training: Partitioning Schemes in Good Shape for Federated Data Grids

In federated Data Grids, individual institutions share their data sets within a community to enable collaborative data analysis. Data access needs to be provided in a scalable fashion since in most e-science communities, data sets do not only grow exponentially but also experience an increasing popularity. If data autonomy is retained, each individual institution has to ensure efficient access to its data. Analyzing application-specific data properties (such as data skew) or query characteristics (query patterns) and distributing data within Data Grids accordingly, allows for improved throughput for data-intensive applications and enables better load-balancing between shared resources. We propose a framework for investigating application-specific index structures for creating suitable partitioning schemes. We evaluate two variants of the well-known Quadtree data structure as well as the Zones approach, an index structure from the astrophysics domain, according to several criteria. Our framework improves data access within federated Data Grids and can be combined with well-established Grid methods as well as with more flexible P2P technologies.

Workload-aware data partitioning in community-driven data grids

Collaborative research in various scientific disciplines requires support for scalable data management enabling the efficient correlation of globally distributed data sources. Motivated by the expected data rates of upcoming projects and a growing number of users, communities explore new data management techniques for achieving high throughput. Community-driven data grids deliver such high-throughput data distribution for scientific federations by partitioning data according to application-specific data and query characteristics. Query hot spots are an important and challenging problem in this environment. Existing approaches to load-balancing from Peer-to-Peer (P2P) data management and sensor networks do not directly meet the requirements of a data-intensive e-science environment. In this paper, our contributions are partitioning schemes based on multi-dimensional index structures enabling communities to trade off data load balancing and handling query hot spots via splitting and replication. We evaluate the partitioning schemes with two typical kinds of data sets from the astrophysics domain and workloads extracted from Sloan Digital Sky Survey (SDSS) query traces and perform throughput measurements in real and simulated networks. The experiments demonstrate the improved workload distribution capabilities and give promising directions for the development of future community grids.

AstroGrid-D: Grid technology for astronomical science

Abstract We present status and results of AstroGrid-D, a joint effort of astrophysicists and computer scientists to employ grid technology for scientific applications. AstroGrid-D provides access to a network of distributed machines with a set of commands as well as software interfaces. It allows simple use of computer and storage facilities and to schedule or monitor compute tasks and data management. It is based on the Globus Toolkit middleware (GT4). Chapter 1 describes the context which led to the demand for advanced software solutions in Astrophysics, and we state the goals of the project. We then present characteristic astrophysical applications that have been implemented on AstroGrid-D in chapter 2. We describe simulations of different complexity, compute-intensive calculations running on multiple sites (Section 2.1 ), and advanced applications for specific scientific purposes (Section 2.2 ), such as a connection to robotic telescopes (Section 2.2.3 ). We can show from these examples how grid execution improves e.g. the scientific workflow. Chapter 3 explains the software tools and services that we adapted or newly developed. Section 3.1 is focused on the administrative aspects of the infrastructure, to manage users and monitor activity. Section 3.2 characterises the central components of our architecture: The AstroGrid-D information service to collect and store metadata, a file management system, the data management system, and a job manager for automatic submission of compute tasks. We summarise the successfully established infrastructure in chapter 4, concluding with our future plans to establish AstroGrid-D as a platform of modern e-Astronomy.

Collaborative query coordination in community-driven data grids

E-science communities face huge data management challenges due to large existing data sets and expected data rates from forthcoming projects. Community-driven data grids provide a scalable, high-throughput oriented data management solution for scientific federations by employing domain-specific partitioning schemes and parallelism. In this paper, we present how community-driven data grids can adapt their query coordination strategies in the face of different typical submission scenarios. We explore the impact of submitting queries uniformly or having submission hot spots. By an extensive evaluation of five strategies on simulated and distributed setups, we show that some coordination strategies are preferable to others, regardless of submission skew. Based on our results, we can improve the usability and scalability of community-driven data grids for data-intensive applications.

Community-driven data grids

Beyond already existing huge data volumes, e-science communities face major challenges in managing the anticipated data deluge of forthcoming projects. Community-driven data grids target at domain-specific federations and provide a distributed, collaborative data management by employing dominant data characteristics (e. g., data skew) and query patterns to optimize the overall throughput. By combining well-established techniques for data partitioning and replication with Peer-to-Peer (P2P) technologies we can address several challenging problems: data load balancing, handling of query hot spots, and the adaption to short-term burst as well as long-term load redistributions.