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Cloud computing has seen tremendous growth, particularly for commercial web applications. The on-demand, pay-as-you-go model creates a flexible and cost-effective means to access compute resources. For these reasons, the scientific computing community has shown increasing interest in exploring cloud computing. However, the underlying implementation and performance of clouds are very different from those at traditional supercomputing centers. It is therefore critical to evaluate the performance of HPC applications in today's cloud environments to understand the tradeoffs inherent in migrating to the cloud. This work represents the most comprehensive evaluation to date comparing conventional HPC platforms to Amazon EC2, using real applications representative of the workload at a typical supercomputing center. Overall results indicate that EC2 is six times slower than a typical mid-range Linux cluster, and twenty times slower than a modern HPC system. The interconnect on the EC2 cloud platform severely limits performance and causes significant variability.

n e Grid2003 Project has deployed a multi-virfual organization, application-driven grid laboratory ('"Grid3'7 that has sustained for several months the production-level services required by physics experiments of the Large Hadron Collider at CERN (ATLAS and CMS), the Sloan Digital Sky Survey project, the gravitational wave search experiment LIGO, the BTeV mperiment at Fermilab, as well as applications in molecular structure analysis and genome analysis, and computer science research projects in such areas as job and data scheduling. The deployed infiastmcture has been operating since Noisniber 2003 with 27 sites, apeak of 2800 processors, work loads fiom 10 different applications exceeding 1300 simultaneous jobs, and data transfers among sites of greater than 2 TBiday. We describe the principles that have guided the development of this unique infrastructure and the practical experiences that have resultedfiom its creation and use. We discuss application requirements for grid services deployment and con$guration. monitoring infiastnic fure, application performance, metrics. and operational experiences. We also summarize lessons learned.