Carrier-grade networks of the future are currently being standardized and designed under the umbrella name of Next Generation Network (NGN). The goal of NGN is to provide a more flexible network infrastructure that supports not just data and voice traffic routing, but also higher level services and interfaces for thirdparty enhancements. Within this paper, opportunities to integrate grid and cloud computing

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The collective and coordinated usage of distributed resources for problem solution within dynamic virtual organizations can be realized with the Grid computing technology. For distributing and solving a task, a Grid application involves a complex workflow of dividing a task into smaller sub-tasks, scheduling and submitting jobs for solving those sub-tasks, and eventually collecting and combining the results of the sub-tasks into a final result. The quality assurance of Grid applications is a challenge due to the highly distributed nature of the Grid environment in which the Grid application is deployed. This paper investigates the applicability of the Testing and Test Control Notation (TTCN-3) for testing the workflows of distributed Grid applications. To this aim, a case study has been created that consists of a distributed Grid application which includes a typical Grid application workflow; as the main contribution, this case study contains a corresponding distributed TTCN-3 test suite that tests the correct execution of the Grid application workflow. To demonstrate the adaptation of the abstract TTCN-3 test suite to a specific Grid environment, corresponding reusable test adapters have been implemented for the Grid middleware Globus Toolkit 4 (GT4). The realized test system demonstrates that TTCN-3 is applicable for testing the workflow of distributed Grid applications.

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At the Large Hadron Collider (LHC) High Energy Physics (HEP) experiment at CERN, 15 PB of raw data is recorded per year. As it was considered inconvenient to store, access and process this data using the traditional hardware and software tools, this data gets reduced to 10-200 TB per year. This paper investigates the applicability of the MapReduce paradigm for analyzing HEP data. In a case study, a sample HEP analysis that makes use of the HEP analysis framework ROOT has been re-implemented using the MapReduce implementation Apache Hadoop. In addition, a Hadoop input format has been developed that takes storage locality of the ROOT file format into account. This approach was evaluated in a cloud computing environment and compared to data analysis with the Parallel ROOT Facility (PROOF).