Several scientific and industry applications require High Performance Computing (HPC) resources to process and or simulate complex models. Not long ago, companies, research institutes, and universities used to acquire and maintain on-premise computer clusters; but, recently, cloud computing has emerged as an alternative for a subset of HPC applications. This poses a challenge to end-users, who have to decide where to run their jobs: on local clusters or burst to a remote cloud service provider. While current research on HPC cloud has focused on comparing performance of on-premise clusters against cloud resources, we build on top of existing efforts and introduce an advisory service to help users make this decision considering the trade-offs of resource costs, performance, and availability on hybrid clouds. We evaluated our service using a real test-bed with a seismic processing application based on Full Waveform Inversion; a technique used by geophysicists in the oil & gas industry and earthquake prediction. We also discuss how the advisor can be used for other applications and highlight the main lessons learned constructing this service to reduce costs and turnaround times.
Following a new phase of HPC cloud studies, Mateescu @cite_5 evaluated a set of benchmarks and complex HPC applications on a range of platforms, both in-house and in the cloud. The studies showed cloud effectiveness for such applications mainly in the case of complementing supercomputers. Gupta and Milojicic @cite_18 highlighted that cloud can be suitable for some HPC applications, but not all. The same group of authors @cite_17 performed a set of experiments using benchmarks and complex HPC applications on various platforms, including supercomputers and clouds, to answer the question why and who should choose cloud for HPC, for what applications, and how should cloud be used for HPC?''.
@cite_5 @cite_18 @cite_17
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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. The use of High Performance Computing (HPC) in commercial and consumer IT applications is becoming popular. HPC users need the ability to gain rapid and scalable access to high-end computing capabilities. Cloud computing promises to deliver such a computing infrastructure using data centers so that HPC users can access applications and data from a Cloud anywhere in the world on demand and pay based on what they use. However, the growing demand drastically increases the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high energy cost which will reduce the profit margin of Cloud providers, but also high carbon emissions which are not environmentally sustainable. Hence, there is an urgent need for energy-efficient solutions that can address the high increase in the energy consumption from the perspective of not only the Cloud provider, but also from the environment. To address this issue, we propose near-optimal scheduling policies that exploit heterogeneity across multiple data centers for a Cloud provider. We consider a number of energy efficiency factors (such as energy cost, carbon emission rate, workload, and CPU power efficiency) which change across different data centers depending on their location, architectural design, and management system. Our carbon energy based scheduling policies are able to achieve on average up to 25 of energy savings in comparison to profit based scheduling policies leading to higher profit and less carbon emissions. 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. In this paper, we report on the experimental results of running a large, tightly coupled, distributed multiscale computation over a hybrid High Performance Computing (HPC) infrastructures. We connected EC2 based cloud clusters located in USA to university clusters located in Switzerland. We ran a concurrent multiscale MPI based application on this infrastructure and measured the overhead induced by extending our HPC clusters with EC2 resources. Our results indicate that accommodating some parts of the multiscale computation on cloud resources can lead to low performance without a proper adjustment of CPUs power and workload. However, by enforcing a load-balancing strategy one can benefit from the extra Cloud resources. We connect an EC2-cloud cluster to a university cluster located in Switzerland.We run a distributed multiscale CFD computation over this extended infrastructure.We evaluate and compare the distributed execution to a local execution.We describe our experience of running parallel CFD application on hybrid platforms.Multiscale computation on cloud requires an adjustment of CPUs power and workload.