Enabling Computational Steering with an Asynchronous-Iterative Computation Framework

In this paper, we present a framework that enables scientists to steer computations executing over large-scale grid computing environments. By using computational steering, users can dynamically control their simulations or computations to reach expected results more efficiently. The framework supports steerable applications by introducing an asynchronous iterative MapReduce programming model that is deployed using Hadoop over a set of virtual machines executing on a multi-cluster grid. To tolerate the heterogeneity between different sites, results are collected asynchronously and users can dynamically interact with their computations to adjust the area of interest. According to users' dynamic interaction, the framework can redistribute the computational overload between the heterogeneous sites and explore the user's interest area by using more powerful sites when possible. With our framework, the bottleneck induced by synchronisation between different sites is considerably avoided, and therefore the response to users' interaction is satisfied more efficiently. We illustrate and evaluate this framework with a scientific application that aims to fit models of the Milky Way galaxy structure to stars observed by the Sloan Digital Sky Survey.