Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

BEAT: An Open-Source Web-Based Open-Science Platform

With the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques promising improved performance, generalization and robustness. Sadly, result reproducibility is often an overlooked feature accompanying original research publications, competitions and benchmark evaluations. The main reasons behind such a gap arise from natural complications in research and development in this area: the distribution of data may be a sensitive issue; software frameworks are difficult to install and maintain; Test protocols may involve a potentially large set of intricate steps which are difficult to handle. Given the raising complexity of research challenges and the constant increase in data volume, the conditions for achieving reproducible research in the domain are also increasingly difficult to meet. To bridge this gap, we built an open platform for research in computational sciences related to pattern recognition and machine learning, to help on the development, reproducibility and certification of results obtained in the field. By making use of such a system, academic, governmental or industrial organizations enable users to easily and socially develop processing toolchains, re-use data, algorithms, workflows and compare results from distinct algorithms and/or parameterizations with minimal effort. This article presents such a platform and discusses some of its key features, uses and limitations. We overview a currently operational prototype and provide design insights.Comment: References to papers published on the platform incorporate

Does size really matter: a review of the role of stake and prize levels in relation to gambling-related harm

Regulatory and industry decisions influencing commercial gambling activities require clear understanding of the role that stakes and prizes play in the development and facilitation of gambling-related harm. Although industry proponents argue for increases in stakes and prizes to meet market demands, regulators remain cautious about the potential implication for gambling-related harm, while industry opponents generally condemn relaxing aspects of gambling policies. To inform this debate, this paper provides a critical examination of the relevant literature. From the review, it is concluded that limitations of the existing literature restrict our ability to draw definitive conclusions regarding the effects of stake and prize variables. Most studies contain multiple, methodological limitations, the most significant of which are diluted risk and reward scenarios used in analogue research settings not reflective of real gambling situations. In addition, there is a lack of conceptual clarity regarding many constructs, particularly the parameters defining jackpots, and the interactive nature and effect of the differing configurations of game parameters and environments are often not taken into consideration when investigating changes to one or more variables. Notwithstanding these limitations, there is sufficient evidence to suggest that stake and prize levels merit consideration in relation to harm minimisation efforts. However, substantial knowledge gaps currently exist, particularly in relation to understanding staking and prize thresholds for risky behaviour, how the impact of stakes and prizes change depending on the configuration and interaction of other game characteristics, and the role of individual and situational determinants. Based on the potential risk factors and the implications for commercial appeal, a player-focussed harm minimisation response may hold the most promise for future research and evaluation in jurisdictions where gambling is a legal and legitimate leisure activity

ClouNS - A Cloud-native Application Reference Model for Enterprise Architects

The capability to operate cloud-native applications can generate enormous business growth and value. But enterprise architects should be aware that cloud-native applications are vulnerable to vendor lock-in. We investigated cloud-native application design principles, public cloud service providers, and industrial cloud standards. All results indicate that most cloud service categories seem to foster vendor lock-in situations which might be especially problematic for enterprise architectures. This might sound disillusioning at first. However, we present a reference model for cloud-native applications that relies only on a small subset of well standardized IaaS services. The reference model can be used for codifying cloud technologies. It can guide technology identification, classification, adoption, research and development processes for cloud-native application and for vendor lock-in aware enterprise architecture engineering methodologies