Hi All, All of us clearly know what Airavata software is about in varying details, but at the same time I realize not every one of us on the list have a full understanding of the architecture as a whole and sub-components. Along with inheriting the code donation, I suggest we focus on bringing every one to speed by means of high level and low level architecture diagrams. I will start a detailed email thread about this task. In short, currently the software assumes understanding of e-Science in general and some details of Grid Computing. Our first focus should be to bring the software to a level any java developer can understand and contribute. Next the focus can be to make it easy for novice users.
I thought a good place to start might be to list out the high level goals and then focus on the first goal with detailed JIRA tasks. I am assuming you will steer us with a orthogonal roadmap to graduation. I hope I am not implying we need to meet the following goals to graduate, because some of them are very open ended. Also, please note that Airavata may have some of these features already, I am mainly categorizing so we will have a focused effort in testing, re-writing or new implementations. Airavata high level feature list: Phase 1: Construct, Execute and monitor workflows from pre-deployed web services. The workflow enactment engine will be the inherent Airavata Workflow Interpreter. Register command line applications as web services, construct and execute workflows with these application services. The applications may run locally, on Grid enabled resources or by ssh'ing to a remote resource. The client to test this phase workflows can be Airavata Workflow Client (XBaya) running as a desktop application. Phase 2: Execute all of phase 1 workflows on Apache ODE engine by generating and deploying BPEL. Develop and deploy gadget interfaces to Apache Rave container to support application registration, workflow submission and monitoring components. Support applications running on virtual machine images to be deployed to Amazon EC2, EUCALYPTUS and similar infrastructure-as-a-service cloud deployments. Phase 3: Expand the compute resources to Elastic Map Reduce and Hadoop based executions. Focus on the data and metadata catalog integration like Apache OODT. I will stop here, to allow us to discuss the same. Once we narrow down on the high level phase 1 goals, I will start a detailed discussion on where the code is now and the steps to get to goal1. Comments, Barbs? Suresh
