Chathura Herath

Axis2, Middleware for Next Generation Web Services

Axis2, the next generation of Apache Web services middleware, is an effort to re-architecture Apache Web service stack to incorporate the changes in Web services. Among many improvements, Axis2 provides first class messaging and SOAP extension supports together with a novel lightweight streaming based XML processing model. The architecture is build on top of a simple and extensible core that provides the basic abstractions for the rest of the system. We present the design and the thought process behind the key abstractions by breaking down the architecture into three topics, XML processing model, extensible SOAP processing model and messaging framework. This paper explains the overall architecture while concentrating on the three topics, and demonstrate how they all fit together to yield Axis2

Apache airavata: a framework for distributed applications and computational workflows

In this paper, we introduce Apache Airavata, a software framework to compose, manage, execute, and monitor distributed applications and workflows on computational resources ranging from local resources to computational grids and clouds. Airavata builds on general concepts of service-oriented computing, distributed messaging, and workflow composition and orchestration. This paper discusses the architecture of Airavata and its modules, and illustrates how the software can be used as individual components or as an integrated solution to build science gateways or general-purpose distributed application and workflow management systems.

WS-Messenger: a Web services-based messaging system for service-oriented grid computing

A Web services-based publish/subscribe system has the potential to create an Internet scale interoperable event notification system which is important for grid computing as it evolves a service-oriented architecture. WS-Messenger is designed to be a Web services-based message broker that can decouple event producers and event consumers and achieve scalable, reliable and efficient message delivery. In this paper, we discuss some challenges that are unique to Web services-based publish/subscribe systems and the key features that distinguish WS-Messenger from other existing message brokers. We then present the architecture and the technology used in WS-Messenger. Performance tests indicate WS-Messenger performs better than the WS-Notification implementation in Globus Toolkit 4 (GT4) and it can be used as a complement to GT4 to improve its scalability. We lastly describe its application to grid workflow orchestration in the LEAD project.

BioVLAB-MMIA: A Cloud Environment for microRNA and mRNA Integrated Analysis (MMIA) on Amazon EC2

MicroRNAs, by regulating the expression of hundreds of target genes, play critical roles in developmental biology and the etiology of numerous diseases, including cancer. As a vast amount of microRNA expression profile data are now publicly available, the integration of microRNA expression data sets with gene expression profiles is a key research problem in life science research. However, the ability to conduct genome-wide microRNA-mRNA (gene) integration currently requires sophisticated, high-end informatics tools, significant expertise in bioinformatics and computer science to carry out the complex integration analysis. In addition, increased computing infrastructure capabilities are essential in order to accommodate large data sets. In this study, we have extended the BioVLAB cloud workbench to develop an environment for the integrated analysis of microRNA and mRNA expression data, named BioVLAB-MMIA. The workbench facilitates computations on the Amazon EC2 and S3 resources orchestrated by the XBaya Workflow Suite. The advantages of BioVLAB-MMIA over the web-based MMIA system include: 1) readily expanded as new computational tools become available; 2) easily modifiable by re-configuring graphic icons in the workflow; 3) on-demand cloud computing resources can be used on an “as needed” basis; 4) distributed orchestration supports complex and long running workflows asynchronously. We believe that BioVLAB-MMIA will be an easy-to-use computing environment for researchers who plan to perform genome-wide microRNA-mRNA (gene) integrated analysis tasks.

Streamflow Programming Model for Data Streaming in Scientific Workflows

Geo-sciences involve large-scale parallel models, high resolution real time data from highly asynchronous and heterogeneous sensor networks and instruments, and complex analysis and visualization tools. Scientific workflows are an accepted approach to executing sequences of tasks on scientists’ behalf during scientific investigation. Many geo-science workflows have the need to interact with sensors that produce large continuous streams of data, but programming models provided by scientific workflows are not equipped to handle continuous data streams. This paper proposes a framework that utilizes scientific workflow infrastructure and the benefits of complex event processing to compensate for the impedance mismatch between scientific workflows and continuous data streams. Further we propose and formalize new workflow semantics that would allow the users to not only incorporate stream in scientific workflow, but also make use of the functionalities provided by the complex event processing systems effective within the scientific workflows.

Experience with adapting a WS-BPEL runtime for eScience workflows

Scientists believe in the concept of collective intelligence and are increasingly collaborating with their peers, sharing data and simulation techniques. These collaborations are made possible by building eScience infrastructures. eScience infrastructures build and assemble various scientific workflow and data management tools which provide rich end user functionality while abstracting the complexities of many underlying technologies. For instance, workflow systems provide a means to execute complex sequence of tasks with or without intensive user intervention and in ways that support flexible reordering and reconfiguration of the workflow. As the workflow technologies continue to emerge, the need for interoperability and standardization clamorous. The Web Services Business Process Execution Language (WS-BPEL) provides one such standard way of defining workflows. WS-BPEL specification encompasses broad range of workflow composition and description capabilities that can be applied to both abstract as well as concrete executable components. Scientific workflows with their agile characteristics present significant challenges in embracing WS-BPEL for eScience purposes. In this paper we discuss the experiences in adopting a WS-BPEL runtime within an eScience infrastructure with reference to an early implementation of a custom eScience motivated BPEL like workflow engine. Specifically the paper focuses on replacing the early adopter research system with a widely used open source WS-BPEL runtime, Apache ODE, while retaining the interoperable design to switch to any WS-BPEL compliant workflow runtime in future. The paper discusses the challenges encountered in extending a business motivated workflow engine for scientific workflow executions. Further, the paper presents performance benchmarks for the developed system.

Effectiveness of Hybrid Workflow Systems for Computational Science

Abstract The workflow and its supporting systems are integral to computational science. Tailored to loosely coupled, and largely coarse-grained tasks, the workflow replaces the script as a way to automate the multiple steps of a large scale model. Workflow reuse has been at the subworkflow level but this restricts, over the long run, a workflow to running on the system on which it was developed. A scientist wanting to use two workflows developed by two different people and for different workflow systems will need to have access to both workflow systems. The contribution this paper makes is a qualitative and quantitative study of the tradeoffs of a hybrid workflow solution that utilizes multiple workflow systems and solutions to execute a single workflow. Our results indicate that the major tradeoffs are not in performance as much as they are in complexity.

Data provenance for preservation of digital geoscience data

A necessary first step in the preservation of digital scientific data is gathering enough information “about” a scientific outcome or data collection, that it can be discovered and used a decade from now as easily as it is reused next week. Data provenance, or lineage of a collection, can capture how a particular scientific collection was created, when and by whom. Our goal is to devise tools automate the collection of provenance so that this task does not fall onto the researcher, and to efficiently store and represent the provenance data that makes the data more amenable to long term preservation. We demonstrate through application to several projects that automated provenance collection can reach the level of necessary provenance but challenges remain in addressing provenance collection in a non-workflow setting, and in data preservation in cyberinfrastructure architectures.

Web Service andWorkflow Abstractions to Large Scale Nuclear Physics Calculations

This paper discusses the web service and scientific workflow abstractions to next generation ab initio computational nuclear physics resources as part of the Leadership Class Configuration Interaction (LCCI) Environment. These abstractions will rapidly and efficiently involve new collaborators and graduate students in productive research. The workflow infrastructure democratizes the access to the nuclear physics simulations executing on remote supercomputing resources. The paper focuses on employing an open community based workflow system in developing and deploying LCCI infrastructures. The paper also emphasizes on the enhancements made to infrastructure to add advanced workflow capabilities providing greater flexibility in handling parametric sweeps and provenance aware workflows. The paper discusses on how the provenance integration will not only capture execution trace but dynamically modify the workflow graph at run time to re-use retrospective execution results.

Open Grid Computing Environment's Workflow Suite for E-Science Projects

E-Science users will want to construct, share, execute and monitor sequence of tasks. These tasks may execute on machines ranging from their local workstations to high-end, grid-enabled compute resources. Often, these tasks are legacy applications written in various programming and scripting languages and are designed to be run in a single user environment rather than as a Web application. These tasks often need to be tied together into composite applications that need to span multiple computing resources. The majority of the scientific experiments in E-Science involve orchestrating multiple tasks in the correct fashion to produce scientific computational experiments. Scientific workflows have proven to be a coherent and abstract framework that is capable of capturing such scientific experiments and hence have gained popularity among the scientific community. Many of the current E-Science projects have adopted different workflow systems and consequently have adopted custom workflow execution and description standards. We argue that the core of the execution semantics used by most scientific workflows can be expressed as a small common set, and we further investigate the possibility of having a higher level workflow description language that is capable of capturing and possibly bridging these different workflow systems. In this demonstration we present a set of E-Science development tools for managing these problems: a high level workflow composition monitoring and enactment tool, a toolkit that is capable of providing Web service interfaces for command line scientific applications, and a service registry that is used as a repository for sharing. This domain independent workflow suite will allow users from wide verity of environments to selectively and securely share their applications as Web services and construct workflows with these services. The suite also features on-demand service creation, workflow orchestration and monitoring. Furthermore, these components can be used individually or collectively to build a small to large-scale E-Science infrastructures.