Quality of service based data-aware scheduling

Distributed supercomputers have been widely used for solving complex computational problems and modeling complex phenomena such as black holes, the environment, supply-chain economics, etc. In this work we analyze the use of these distributed supercomputers for time sensitive data-driven applications. We present the scheduling challenges involved in running deadline sensitive applications on shared distributed supercomputers running large parallel jobs and introduce a ``data-aware\u27\u27 scheduling paradigm that overcomes these challenges by making use of Quality of Service classes for running applications on shared resources. We evaluate the new data-aware scheduling paradigm using an event-driven hurricane simulation framework which attempts to run various simulations modeling storm surge, wave height, etc. in a timely fashion to be used by first responders and emergency officials. We further generalize the work and demonstrate with examples how data-aware computing can be used in other applications with similar requirements

A Day in the Life of a Grid-Enabled Application: Counting on the Grid

While Grid technologies are maturing rapidly, there still remains a shortage of real Grid applications. One important reason is the lack of simple and high-level application programming interfaces to the Grid, bridging the gap between existing Grid middleware and application-level needs. The Grid Application Toolkit (GAT), as currently developed by the EC-funded project GridLab [1], provides a unified, simple programming interface to the Grid infrastructure, tailored to the needs of Grid application programmers and users. In this paper, we outline a motivating use case, present the GAT API functionality, and sketch existing bindings to programming languages and their implementations.

Resgrid: A grid-aware toolkit for reservoir uncertainty analysis

Many efforts in Grid communities have focused on middleware research and development. However, Grid application-level tools are needed which can build higherlevel functionality on top of core middleware services. We work with specific classes of scientific applications and present a Grid-aware toolkit ResGrid for reservoir uncertainty analysis. With the help of the ResGrid, a reservoir engineer can transparently take advantage of Grid resources and services for compute-intensive and dataintensive uncertainty analysis as well as enforce the understanding of multiphase reservoir modeling. This paper explains a typical reservoir uncertainty analysis scenario and evaluates the current limitations a reservoir engineer faces. The ResGrid is introduced in terms of overview, architecture, implementation status. In the case studies, the design and implementation of the ResGrid are verified via a reservoir uncertainty analysis process on the CCT Grid testbed. The ResGrid releases the unbound capabilities of the Grid and improves the efficiency for reservoir researchers. The ResGrid is also utilized by other application areas, such as coastal modeling. 1

Design of the futuregrid experiment management framework

Abstract—FutureGrid provides novel computing capabilities that enable reproducible experiments while simultaneously supporting dynamic provisioning. This paper describes the Future-Grid experiment management framework to create and execute large scale scientific experiments for researchers around the globe. The experiments executed are performed by the various users of FutureGrid ranging from administrators, software developers, and end users. The Experiment management framework will consist of software tools that record user and system actions to generate a reproducible set of tasks and resource configurations. Additionally, the experiment management framework can be used to share not only the experiment setup, but also performance information for the specific instantiation of the experiment. This makes it possible to compare a variety of experiment setups and analyze the impact Grid and cloud software stacks have. I

Distributed and collaborative visualization of large data sets using high-speed

network