D Cooperating Services for Data-driven Computational Experimentation

characterized by dynamic adaptation in response to external data. Applications of this type, which are often dataand I/O-intensive, run as parallel or distributed computations on high-end resources such as distributed clusters or symmetric multiprocessing machines. On-demand weather forecasting is a canonical example of a data-driven application. As the article “Service-Oriented Environments in Research and Education for Dynamically Interacting with Mesoscale Weather” (pp. XX–YY in this issue) describes, on-demand weather forecasting is the automated process of invoking a forecast model run in response to the detection of a severe weather condition. The existing framework for running forecast models has drawbacks that we must overcome to bring about dynamic on-demand forecasting. For instance, the current investigation process requires a lot of human involvement, particularly in the staging and moving of the files required by the model and in the invocation of downstream tools to visualize and analyze model results. Although scripts exist to automate some of the process, a knowledgeable expert must properly configure them. The Linked Environments for Atmospheric Discovery (LEAD) project addresses the limitations of current weather forecast frameworks through a new, service-oriented architecture capable of responding to unpredicted weather events and response patterns in real time. These services are intended to support the execution of multimodel simulations of weather forecasts on demand across a distributed Grid of resources while dynamically adapting resource allocation in response to the results. At the system’s heart is a suite of core services that together provide the essential functionality needed to invoke and run a complex experiment with minimal human involvement. Specifically, it lets the user define an experiment workflow, execute the experiment, and store the results. (See the “Related Work” sidebar for a discussion of other work in this area.) In this article, we focus on three services—the MyLEAD metadata catalog service, notification service, and workflow service—that together form the core services for managing complex experimental meteorological investigations and managing the data products used in and generated during the computational experimentation. We show how