No abstract

Emerging serverless computing technologies, such as function as a service (FaaS), enable developers to virtualize the internal logic of an application, simplifying the management of cloud-native services and allowing cost savings through billing and scaling at the level of individual functions. Serverless computing is therefore rapidly shifting the attention of software vendors to the challenge of developing cloud applications deployable on FaaS platforms. In this vision paper, we present the research agenda of the RADON project (http://radon-h2020.eu), which aims to develop a model-driven DevOps framework for creating and managing applications based on serverless computing. RADON applications will consist of fine-grained and independent microservices that can efficiently and optimally exploit FaaS and container technologies. Our methodology strives to tackle complexity in designing such applications, including the solution of optimal decomposition, the reuse of serverless functions as well as the abstraction and actuation of event processing chains, while avoiding cloud vendor lock-in through models.

In this study, we examined the thickness of the electrical double layer (EDL) in ionic liquids using density functional theory (DFT) calculations and molecular dynamics (MD) simulations. We focused on BF4- anion adsorption from the 1-ethyl-3-methylimidazolium tetrafluoroborate (EMImBF4) ionic liquid on the Au(111) surface. At both DFT and MD levels, we evaluated the capacitance-potential dependence for the Helmholtz model of the interface. Using MD simulations, we also explored a more realistic, multilayer EDL model accounting for the ion layering. Concurrent analysis of the DFT and MD results provides a ground for thinking whether the electrical double layer in ionic liquids is one- or multi-ionic-layer thick.

Abstract. Cloud computing, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. To study the effects of moving parallel scientific applications onto the cloud, we deployed several benchmark applications like matrix-vector operations and NAS parallel benchmarks, and DOUG (Domain decomposition On Unstructured Grids) on the cloud. DOUG is an open source software package for parallel iterative solution of very large sparse systems of linear equations. The detailed analysis of DOUG on the cloud showed that parallel applications benefit a lot and scale reasonable on the cloud. We could also observe the limitations of the cloud and its comparison with cluster in terms of performance. However, for efficiently running the scientific applications on the cloud infrastructure, the applications must be reduced to frameworks that can successfully exploit the cloud resources, like the MapReduce framework. Several iterative and embarrassingly parallel algorithms are reduced to the MapReduce model and their performance is measured and analyzed. The analysis showed that Hadoop MapReduce has significant problems with iterative methods, while it suits well for embarrassingly parallel algorithms. Scientific computing often uses iterative methods to solve large problems. Thus, for scientific computing on the cloud, this paper raises the necessity for better frameworks or optimizations for MapReduce.

No abstract