Enabling computational steering with an asynchronous-iterative computation framework

Abstract

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 ef ciently. 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..