Data access and integration in the ISPIDER proteomics grid

Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources

Adaptive workflow nets for grid computing

Existing grid applications commonly use workflows for the orchestration of grid services. Existing workflow models however suffer from the lack of adaptivity. In this paper we define Adaptive Grid Workflow nets (AGWF nets) appropriate for modeling grid workflows and allowing changes in the process structure as a response to triggering events/exceptions. Moreover, a recursion is allowed, which makes the model especially appropriate for a number of grid applications. We show that soundness can be verified for AGWF nets

Adaptive Workflow Nets for Grid Computing

Abstract. Existing grid applications commonly use workflows for the orchestration of grid services. Existing workflow models however suf-fer from the lack of adaptivity. In this paper we define Adaptive Grid Workflow nets (AGWF nets) appropriate for modeling grid workflows and allowing changes in the process structure as a response to trigger-ing events/exceptions. Moreover, a recursion is allowed, which makes the model especially appropriate for a number of grid applications. We show that soundness can be verified for AGWF nets

Starflow: A script-centric data analysis environment

Abstract. We introduce StarFlow, a script-centric environment for data analysis. StarFlow has four main features: (1) extraction of control and data-flow dependencies through a novel combination of static analysis, dynamic runtime analysis, and user annotations, (2) command-line tools for exploring and propagating changes through the resulting dependency network, (3) support for workflow abstractions enabling robust parallel executions of complex analysis pipelines, and (4) a seamless interface with the Python scripting language. We describe real applications of StarFlow, including automatic parallelization of complex workflows in the cloud. Key words: automatic parallelization, automatic updating, computational workflows, control flow, data-flow, data analysis, dependency tracking, provenance, Python, workflow abstraction

Steady-State for Batches of Identical Task Trees

In this paper, we focus on the problem of scheduling batches of identical task graphs on a heterogeneous platform, when the task graph consists in a tree. We rely on steady-state scheduling, and aim at reaching the optimal throughput of the system. Contrarily to previous studies, we concentrate upon the scheduling of batches of limited size. We try to reduce the processing time of each instance, thus making steady-state scheduling applicable to smaller batches. The problem is proven NP-complete, and a mixed integer program is presented to solve it. Then, different solutions, using steady-state scheduling or not, are evaluated through comprehensive simulations

Actor-Oriented Design of Scientific Workflows

Scientific workflows are becoming increasingly important as a unifying mechanism for interlinking scientific data management, analysis, simulation, and visualization tasks. Scientific workflow systems are problem-solving environments, supporting scientists in the creation and execution of scientific workflows