Grid computing and beyond: The context of Dynamic Data Driven Applications Systems

The advent of Grid computing has enhanced our capabilities to model and simulate complex systems arising in scientific, engineering, and commercial applications. The premise of Grid computing has been "on-demand" availability of computational resources to an application as needed, in the same manner as electricity is provided readily through electrical power grids. The computational grid (or simply the "Grid") entails ubiquitous access to resources (local or remote), such as computation and communication resources, as well as access to storage systems and visualization systems. As Grid computing technologies mature, it behooves to look beyond the current capabilities, into more advanced future environments. The environments of interest here are the enhanced capabilities that can be created by the paradigm of Dynamic Data Driven Applications Systems (DDDAS). DDDAS entails the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. The DDDAS concept offers the promise of improving application models and methods, and augmenting the analysis and prediction capabilities of application simulations and the effectiveness of measurement systems. Enabling this synergistic feedback and control loop between application simulations and measurements requires novel application modeling approaches and frameworks, algorithms stable under dynamic data injection and steering conditions, and new systems software and computational infrastructure capabilities. Recent advances in complex applications and the advent of Grid computing and sensor systems are some of the technologies that make it timely to embark in developing DDDAS capabilities. DDDAS environments extend the current notion of Grid infrastructure to also include the measurement systems in an integrated and synergistic way. DDDAS environments require support and services that go beyond the current Grid services in terms of the systems software and computational infrastructure technologies. This paper discusses the new capabilities enabled with the DDDAS paradigm and the technology challenges and new advances needed to support such environments, in particular with respect to Grid infrastructure requirements.