SUMMARY With the maturation of grid computing facilities and recent explosion of cloud computing data centers, midscale computational science has more options than ever before to satisfy computational needs. But heterogeneity brings complexity. We propose a simple abstraction for interaction with heterogeneous resource managers spanning grid and cloud computing and on features that make the tool useful for the midscale physical or natural scientist. Key strengths of the abstraction are its support for multiple standard job specification languages, preservation of direct user interaction with the service, removing the delay that can come through layers of services, and the predictable behavior under heavy loads. Copyright © 2012 John Wiley & Sons, Ltd.

Driven by the need to provision resources on demand, scientists are turning to commercial and research test-bed Cloud computing resources to run their scientific experiments. Job scheduling on cloud computing resources, unlike earlier platforms, is a balance between throughput and cost of executions. Within this context, we posit that usage patterns can improve the job execution, because these patterns allow a system to plan, stage and optimize scheduling decisions. This paper introduces a novel approach to utilization of user patterns drawn from knowledge-based techniques, to improve execution across a series of active workflows and jobs in cloud computing environments. Using empirical analysis we establish the accuracy of our prediction approach for two different workloads and demonstrate how this knowledge can be used to improve job executions.

Scientists working in eScience environments often use workflows to carry out their computations. Since the workflows evolve as the research itself evolves, these workflows can be a tool for tracking the evolution of the research. Scientists can trace their research and associated results through time or even go back in time to a previous stage and fork to a new branch of research. In this paper we introduce the workflow evolution framework (EVF), which is demonstrated through implementation in the Trident workflow workbench. The primary contribution of the EVF is efficient management of knowledge associated with workflow evolution. Since we believe evolution can be used for workflow attribution, our framework will motivate researchers to share their workflows and get the credit for their contributions.

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