SWAN (Service for Web based ANalysis) is a platform to perform interactive data analysis in the cloud. SWAN allows users to write and run their data analyses with only a web browser, leveraging on the widely-adopted Jupyter notebook interface. The user code, executions and data live entirely in the cloud. SWAN makes it easier to produce and share results and scientific code, access scientific software, produce tutorials and demonstrations as well as preserve analyses. Furthermore, it is also a powerful tool for non-scientific data analytics.This paper describes how a pilot of the SWAN service was implemented and deployed at CERN. Its backend combines state-of-the-art software technologies with a set of existing IT services such as user authentication, virtual computing infrastructure, mass storage, file synchronisation and sharing, specialised clusters and batch systems.The added value of this combination of services is discussed, with special focus on the opportunities offered by the CERNBox service and its massive storage backend, EOS. In particular, it is described how a cloud-based analysis model benefits from synchronised storage and sharing capabilities.

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Internet energy consumption is rapidly becoming an issue due to the exponential traffic growth and the rapid expansion of communication infrastructures worldwide. In this paper we propose an off-line IP traffic engineering approach that allows to adapt the network energy consumption to different daily traffic scenarios (e.g., night, morning), by switching off and on (putting in sleeping mode and waking up) communication interfaces (links) and entire routers. We focus on routing domains where OSPF (Open Shortest Path First) protocol is adopted and we aim at optimizing energy consumption and network congestion by efficiently configuring the OSPF link weights. We present two heuristics, the Greedy Algorithm for Energy Saving (GA-ES) and the Two-stage Algorithm for Energy Saving (TA-ES). The computational results for three real network topologies show that it is possible to switch off up to 80% of the core nodes during low traffic periods (night hours), while moderately increasing the network congestion.

Based on the observation of low average CPU utilisation of several hundred file storage servers in the EOS storage system at CERN, the Batch on EOS Extra Resources (BEER) project developed an approach to also utilise these resources for batch processing. Initial proof of concept tests showed little interference between batch and storage services on a node. Subsequently a model for production was developed and implemented. This has been deployed on part of the CERN EOS production service. The implementation and test results will be presented. The potential for additional resources at the CERN Tier-0 centre is of the order of ten thousand hardware threads in the near term, as well as being a step towards a hyper-converged infrastructure.