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تسجيل مستخدم جديدR-GMA: First results after deployment
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We describe R-GMA (Relational Grid Monitoring Architecture) which is being developed within the European DataGrid Project as an Grid Information and Monitoring System. Is is based on the GMA from GGF, which is a simple Consumer-Producer model. The special strength of this implementation comes from the power of the relational model. We offer a global view of the information as if each VO had one large relational database. We provide a number of different Producer types with different characteristics; for example some support streaming of information. We also provide combined Consumer/Producers, which are able to combine information and republish it. At the heart of the system is the mediator, which for any query is able to find and connect to the best Producers to do the job. We are able to invoke MDS info-provider scripts and publish the resulting information via R-GMA in addition to having some of our own sensors. APIs are available which allow the user to deploy monitoring and information services for any application that may be needed in the future. We have used it both for information about the grid (primarily to find what services are available at any one time) and for application monitoring. R-GMA has been deployed in Grid testbeds, we describe the results and experiences of this deployment.
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We describe R-GMA (Relational Grid Monitoring Architecture) which has been developed within the European DataGrid Project as a Grid Information and Monitoring System. Is is based on the GMA from GGF, which is a simple Consumer-Producer model. The spe
cial strength of this implementation comes from the power of the relational model. We offer a global view of the information as if each Virtual Organisation had one large relational database. We provide a number of different Producer types with different characteristics; for example some support streaming of information. We also provide combined Consumer/Producers, which are able to combine information and republish it. At the heart of the system is the mediator, which for any query is able to find and connect to the best Producers for the job. We have developed components to allow a measure of inter-working between MDS and R-GMA. We have used it both for information about the grid (primarily to find out about what services are available at any one time) and for application monitoring. R-GMA has been deployed in various testbeds; we describe some preliminary results and experiences of this deployment.
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Application users have now been experiencing for about a year with the standardized resource brokering services provided by the workload management package of the EU DataGrid project (WP1). Understanding, shaping and pushing the limits of the system
has provided valuable feedback on both its design and implementation. A digest of the lessons, and better practices, that were learned, and that were applied towards the second major release of the software, is given.
In a previous paper [1], we presented a three-flavour oscillation analysis of the solar neutrino measurements and of the first data from the KamLAND experiment, in terms of the relevant mass-mixing parameters (delta m^2, theta_12, theta_13). The anal
ysis, performed by including the terrestrial neutrino constraints coming from the CHOOZ (reactor), KEK-to-Kamioka (K2K, accelerator) and Super-Kamiokande (SK, atmospheric) experiments, provided a stringent upper limit on theta_13, namely, sin^2(theta_13)<0.05 at 3 sigma. We reexamine such upper bound in the light of a recent (although preliminary) reanalysis of atmospheric neutrino data performed by the SK collaboration, which seems to shift the preferred value of the largest neutrino square mass difference Delta m^2 downwards. By taking the results of the SK official reanalysis at face value, and by repeating the analysis in [1] with such a new input, we find that the upper bound on theta_{13} is somewhat relaxed: sin^2(theta_13)<0.067 at 3 sigma. Related phenomenological issues are briefly discussed.
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pectrum) and investigate its effect on the allowed region in the $Delta m^2-tan^2theta$ plane. The best-fit from a combined analysis which uses the KamLAND rate plus global solar data comes at $Delta m^2 = 6.06 times 10^{-5}$ eV $^2$ and $tan^2theta=0.42$, very close to the global solar best-fit, leaving a large allowed region within the global solar LMA contour. The inclusion of the KamLAND spectral data in the global fit gives a best-fit $Delta m^2 = 7.15 times 10^{-5}$ eV $^2$ and $tan^2theta=0.42$ and constrains the allowed areas within LMA, leaving essentially two allowed zones. Maximal mixing though allowed by the KamLAND data alone is disfavored by the global solar data and remains disallowed at about $3sigma$. The LOW solution is now ruled out at about 5$sigma$ w.r.t. the LMA solution.
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