High intensive computation applications can usually take days to months to nish an execution. During this time, it is common to have variations of the available resources when considering that such hardware is usually shared among a plurality of researchers departments within an organization. On the other hand, High Performance Clusters can take advantage of Cloud Computing bursting techniques for the execution of applications together with on-premise resources. In order to meet deadlines, high intensive computational applications can use the Cloud to boost their performance when they are data and task parallel. This article presents an ongoing work towards the use of extended resources of an HPC execution platform together with Cloud. We propose an unied view of such heterogeneous environments and a method that monitors, predicts the application execution time, and dynamically shifts part of the domain previously running in local HPC hardware to be computed in the Cloud, meeting then a specic deadline. The method is exemplied along with a seismic application that, at runtime, adapts itself to move part of the processing to the Cloud (in a movement called bursting) and also auto-scales (the moved part) over cloud nodes. Our preliminary results show that there is an expected overhead for performing this movement and for synchronizing results, but the outcomes demonstrate it is an important feature for meeting deadlines in the case an on-premise cluster is overloaded or cannot provide the capacity needed for a particular project.
 HPC applications are increasingly being used in academia and laboratories for scientific research and in industries for business and analytics. Cloud computing offers the benefits of virtualization, elasticity of resources and elimination of cluster setup cost and time to HPC applications users. However, poor network performance, performance variation and OS noise are some of the challenges for execution of HPC applications on Cloud. In this paper, we propose that Cloud can be viable platform for some HPC applications depending upon application characteristics such as communication volume and pattern and sensitivity to OS noise and scale. We present an evaluation of the performance and cost tradeoffs of HPC applications on a range of platforms varying from Cloud (with and without virtualization) to HPC-optimized cluster. Our results show that Cloud is viable platform for some applications, specifically, non communicationintensive applications such as embarrassingly parallel and tree-structured computations up to high processor count and for communication-intensive applications up to low processor count. We introduce a hybrid High Performance Computing (HPC) infrastructure architecture that provides predictable execution of scientific applications, and scales from a single resource to multiple resources, with different ownership, policy, and geographic locations. We identify three paradigms in the evolution of HPC and high-throughput computing: owner-centric HPC (traditional), Grid computing, and Cloud computing. After analyzing the synergies among HPC, Grid and Cloud computing, we argue for an architecture that combines the benefits of these technologies. We call the building block of this architecture, Elastic Cluster. We describe the concept of Elastic Cluster and show how it can be used to achieve effective and predictable execution of HPC workloads. Then we discuss implementation aspects, and propose a new distributed information system design that combines features of distributed hash tables and relational databases.