Data Farming on Heterogeneous Clouds

Abstract

Using multiple Clouds as a single environment to conduct simulation-based virtual experiments at a large-scale is a challenging problem. This paper describes how this can be achieved with the Scalarm platform in the context of data farming. In particular, a use case with a private Cloud combined with public, commercial Clouds is studied. We discuss the current architecture and implementation of Scalarm in terms of supporting different infrastructures, and propose how it can be extended in order to attain a unification of different Clouds usage. We discuss different aspects of the Cloud usage unification including: scheduling virtual machines, authentication, and virtual machine state monitoring. An experimental evaluation of the presented solution is conducted with a genetic algorithm solving the well-known Travel Salesman Problem. The evaluation uses three different resource configurations: using only public Cloud, using only private Cloud, and using both public and private Clouds.