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Cinquilli, M.

Publications and source records attributed to Cinquilli, M..

A gLite FTS based solution for managing user output in CMS

The CMS distributed data analysis workflow assumes that jobs run in a different location from where their results are finally stored. Typically the user output must be transferred across the network from one site to another, possibly on a different continent or over links not necessarily validated for high bandwidth/high reliability transfer. This step is named stage-out and in CMS was originally implemented as a synchronous step of the analysis job execution. However, our experience showed the weakness of this approach both in terms of low total job execution efficiency and failure rates, wasting precious CPU resources. The nature of analysis data makes it inappropriate to use PhEDEx, the core data placement system for CMS. As part of the new generation of CMS Workload Management tools, the Asynchronous Stage-Out system (AsyncStageOut) has been developed to enable third party copy of the user output. The AsyncStageOut component manages glite FTS transfers of data from the temporary store at the site where the job ran to the final location of the data on behalf of that data owner. The tool uses python daemons, built using the WMCore framework, and CouchDB, to manage the queue of work and FTS transfers. CouchDB also provides the platform for a dedicated operations monitoring system. In this paper, we present the motivations of the asynchronous stage-out system. We give an insight into the design and the implementation of key features, describing how it is coupled with the CMS workload management system. Finally, we show the results and the commissioning experience.

Cinquilli, M.↗

CRAB3: Establishing a new generation of services for distributed analysis at CMS

In CMS Computing the highest priorities for analysis tools are the improvement of the end users ability to produce and publish reliable samples and analysis results as well as a transition to a sustainable development and operations model. To achieve these goals CMS decided to incorporate analysis processing into the same framework as data and simulation processing. This strategy foresees that all workload tools (TierO, Tier1, production, analysis) share a common core with long term maintainability as well as the standardization of the operator interfaces. The re-engineered analysis workload manager, called CRAB3, makes use of newer technologies, such as RESTFul based web services and NoSQL Databases, aiming to increase the scalability and reliability of the system. As opposed to CRAB2, in CRAB3 all work is centrally injected and managed in a global queue. A pool of agents, which can be geographically distributed, consumes work from the central services serving the user tasks. The new architecture of CRAB substantially changes the deployment model and operations activities. In this paper we present the implementation of CRAB3, emphasizing how the new architecture improves the workflow automation and simplifies maintainability. In particular, we will highlight the impact of the new design on daily operations.

Cinquilli, M.↗

The CMS workload management system

CMS has started the process of rolling out a new workload management system. This system is currently used for reprocessing and Monte Carlo production with tests under way using it for user analysis. It was decided to combine, as much as possible, the production/processing, analysis and T0 codebases so as to reduce duplicated functionality and make best use of limited developer and testing resources. This system now includes central request submission and management (Request Manager), a task queue for parcelling up and distributing work (WorkQueue) and agents which process requests by interfacing with disparate batch and storage resources (WMAgent).

Cinquilli, M.↗