Amazon S3 for science grids: a viable solution?
DADC '08 Proceedings of the 2008 international workshop on Data-aware distributed computing
Can cloud computing reach the top500?
Proceedings of the combined workshops on UnConventional high performance computing workshop plus memory access workshop
Early observations on the performance of Windows Azure
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Case study for running HPC applications in public clouds
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Distributed systems meet economics: pricing in the cloud
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
Data Sharing Options for Scientific Workflows on Amazon EC2
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Windows Azure Storage: a highly available cloud storage service with strong consistency
SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
The Gfarm File System on Compute Clouds
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
State of the Practice Reports
Octopus: efficient data intensive computing on virtualized datacenters
Proceedings of the 6th International Systems and Storage Conference
Evaluating I/O aware network management for scientific workflows on networked clouds
NDM '13 Proceedings of the Third International Workshop on Network-Aware Data Management
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The emergence of cloud computing brought the opportunity to use large-scale computational infrastructures for a broad spectrum of scientific applications. As more and more cloud providers and technologies appear, scientists are faced with an increasingly difficult problem of evaluating various offerings, like public and private clouds, and deciding which model to use for their applications' needs. In this paper, we make a performance evaluation of two public and private cloud platforms for scientific computing workloads. We compare the Azure and Nimbus clouds, considering all the primary needs of scientific applications (computation power, storage, data transfers and costs). The evaluation is done using both synthetic benchmarks and a real-life application. Our results show that Nimbus incurs less varaibility and has increased support for data intensive applications, while Azure deploys faster and has a lower cost.