Search

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.

Growing role of the cooperation between companies around the world causes the rise of meaning of the SLA agreements which are negotiated among companies. These agreements usually require a reliable and robust monitoring process, since their fulfilment is crucial for the coherence of the cooperation. In this paper we present the application of the ESB integration architecture in the specialized distributed monitoring system - the FiVO SLA monitoring system - which is used to monitor the resources and services shared among the cooperating companies. Different deployment models of this system are proposed and the results from their performance tests are presented.