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.
Computing as a utility has reached the mainstream. Scientists can now rent time on large commercial clusters through several vendors. The cloud computing model provides flexible support for "pay as you go" systems. In addition to no upfront investment in large clusters or supercomputers, such systems incur no maintenance costs. Furthermore, they can be expanded and reduced on-demand in real-time. Current cloud computing performance falls short of systems specifically designed for scientific applications. Scientific computing needs are quite different from those of web applications--composed primarily of database queries--that have been the focus of cloud computing vendors. In this paper we investigate the use of cloud computing for high-performance numerical applications. In particular, we assume unlimited monetary resources to answer the question, "How high can a cloud computing service get in the TOP500 list?" We show results for the Linpack benchmark on different allocations on Amazon EC2. Cloud Computing is emerging today as a commercial infrastructure that eliminates the need for maintaining expensive computing hardware. Through the use of virtualization, clouds promise to address with the same shared set of physical resources a large user base with different needs. Thus, clouds promise to be for scientists an alternative to clusters, grids, and supercomputers. However, virtualization may induce significant performance penalties for the demanding scientific computing workloads. In this work we present an evaluation of the usefulness of the current cloud computing services for scientific computing. We analyze the performance of the Amazon EC2 platform using micro-benchmarks and kernels. While clouds are still changing, our results indicate that the current cloud services need an order of magnitude in performance improvement to be useful to the scientific community.