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 t models of the Milky Way galaxy structure to stars observed by the Sloan Digital Sky Survey.

In this paper, we investigate the benefits that organisations can reap by using "Cloud Computing" providers to augment the computing capacity of their local infrastructure. We evaluate the cost of six scheduling strategies used by an organisation that operates a cluster managed by virtual machine technology and seeks to utilise resources from a remote Infrastructure as a Service (IaaS) provider to reduce the response time of its user requests. Requests for virtual machines are submitted to the organisation's cluster, but additional virtual machines are instantiated in the remote provider and added to the local cluster when there are insufficient resources to serve the users' requests. Naïve scheduling strategies can have a great impact on the amount paid by the organisation for using the remote resources, potentially increasing the overall cost with the use of IaaS. Therefore, in this work we investigate six scheduling strategies that consider the use of resources from the "Cloud", to understand how these strategies achieve a balance between performance and usage cost, and how much they improve the requests' response times.

While many dream and talk about service level agreement (SM) and quality of service (QoS) for service oriented architectures (SOA), the practical reality of grid computing are still far from providing effective techniques enabling such contractual agreements. Towards this goal, this paper provides an overview of the techniques offered by ProActive to set and use contractual agreements. Based on the identification of roles, application developer, infrastructure manager, application user, the actors of a grid environment can specify what is required or what is provided at various levels. The results are both flexibility and adaptability, matching the application constraints and the environment characteristics with various techniques.