Biblio

In many scientific fields, simulations and analyses require compositions of computational entities such as web-services, programs, and applications. In such fields, users may want various trade-offs between different qualities. Examples include: (i) performing a quick approximation vs. an accurate, but slower, experiment, (ii) using local slower execution environments vs. remote, but advanced, computing facilities, (iii) using quicker approximation algorithms vs. computationally expensive algorithms with smaller data. However, such trade-offs are difficult to make as many such decisions today are either (a) wired into a fixed configuration and cannot be changed, or (b) require detailed systems knowledge and experimentation to determine what configuration to use. In this paper we propose an approach that uses architectural models coupled with automated design space generation for making fidelity and timeliness trade-offs. We illustrate this approach through an example in the intelligence analysis domain.