The commissioning of CMS sites: improving the site reliability

The computing system of the CMS experiment works using distributed resources from more than 60 computing centres worldwide. These centres, located in Europe, America and Asia are interconnected by the Worldwide LHC Computing Grid. The operation of the system requires a stable and reliable behaviour of the underlying infrastructure. CMS has established a procedure to extensively test all relevant aspects of a Grid site, such as the ability to efficiently use their network to transfer data, the functionality of all the site services relevant for CMS and the capability to sustain the various CMS computing workflows at the required scale. This contribution describes in detail the procedure to rate CMS sites depending on their performance, including the complete automation of the program, the description of monitoring tools, and its impact in improving the overall reliability of the Grid from the point of view of the CMS computing system

Use of the gLite-WMS in CMS for production and analysis

The CMS experiment at LHC started using the Resource Broker (by the EDG and LCG projects) to submit Monte Carlo production and analysis jobs to distributed computing resources of the WLCG infrastructure over 6 years ago. Since 2006 the gLite Workload Management System (WMS) and Logging \& Bookkeeping (LB) are used. The interaction with the gLite-WMS/LB happens through the CMS production and analysis frameworks, respectively ProdAgent and CRAB, through a common component, BOSSLite. The important improvements recently made in the gLite-WMS/LB as well as in the CMS tools and the intrinsic independence of different WMS/LB instances allow CMS to reach the stability and scalability needed for LHC operations. In particular the use of a multi-threaded approach in BOSSLite allowed to increase the scalability of the systems significantly. In this work we present the operational set up of CMS production and analysis based on the gLite-WMS and the performances obtained in the past data challenges and in the daily Monte Carlo productions and user analysis usage in the experiment