Dynamic Virtualized Deployment of Particle Physics Environments on a High Performance Computing Cluster

The NEMO High Performance Computing Cluster at the University of Freiburg has been made available to researchers of the ATLAS and CMS experiments. Users access the cluster from external machines connected to the World-wide LHC Computing Grid (WLCG). This paper describes how the full software environment of the WLCG is provided in a virtual machine image. The interplay between the schedulers for NEMO and for the external clusters is coordinated through the ROCED service. A cloud computing infrastructure is deployed at NEMO to orchestrate the simultaneous usage by bare metal and virtualized jobs. Through the setup, resources are provided to users in a transparent, automatized, and on-demand way. The performance of the virtualized environment has been evaluated for particle physics applications

MESoR - Management and exploitation of solar resource knowledge

CD-ROMKnowledge of the solar resource is essential for the planning and operation of solar energy systems. A number of data bases giving information on solar resources have been developed over the past years. The result is a fragmentation of services each having each own mechanism of access and all are giving different results due to different methods, input data and base years. The project MESoR, co-funded by the European Commission, reduces the associated uncertainty by setting up standard benchmarking rules and measures for comparing the data bases, user guidance to the application of resource data and unifying access to various data bases

An Elastic Scheduling Algorithm For Resource Co-Allocation Based on System Generated Predictions With Priority

Resource Co-Allocation is basically used to execute multiple site jobs in a large scale computing environments with secure, faultless and in transparent manner. To be precise we are actually allocating multiple resources for different jobs taking into account the time parameter. Here we make use of the Scheduling queue and Resource Co-Allocation to reduce the Turn-around time with an advanced concept of System Generated Prediction based on Priority. In existing works we are scheduling the resource co-allocation request from user runtime estimation. As user runtime estimations are usually very imprecise that is not clear. In proposed work we are scheduling the resource co-allocation request based on system generated predictions through Discovery service & Priority (fairness and user experience) through topological sorting technique. The system generated predictions are better parameters than user runtime estimates for Resource co-Allocation scheduling, because System generated predictions reduce the scheduling time through proxy ser based discovery service technique. The proposed work consider priorities like advanced reservation, system Generated Predictions, Negotiation, Co-scheduling, policy (SLA, Price, Trust) for resource Co-Allocation. The system generated predictions are better than user runtime estimates for Resource co- Allocation scheduling, using the experimental data’s we proved this concept. End User doesn’t want the grid and resource knowledge only submit job to the portal. This proposed portal will take care of all knowledge about the resource collocation automatically with fast and efficient manner