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

Toward an open cloud standard

Today's cloud ecosystem features several increasingly divergent management interfaces. Numerous bridging efforts attempt to ameliorate the resulting vendor lock-in for customers. However, as the number of providers continues to grow, the drawback of this approach becomes apparent: the need to maintain adapter implementations. The Open Cloud Computing Interface builds on the fundamentals of modern Web-based services to define a standardized interface for cloud environments while enabling service providers to differentiate their service offerings at the same time

XSEDE Cloud VM Repository

The Technology Investigation Service's Technology Evaluation team produced a white paper to gather ideas for an XSED sponsored repository that would house VM's centrally. This report summarizes their findings.National Science Foundation OCI-1053575Ope

Data Security Predicament in Cloud

Cloud computing a tremendous technology that today becomes the part of almost everyone�s life. The cloud computing is used in homes, business organizations, in banking industries etc. Today, everyone is using cloud may be ranging from posting their pictures on social networking sites or by storing their crucial information. Although, the cloud is using in different areas, but for using cloud services, everyone faces some challenges associated with cloud. This study enlists some of the challenges of using cloud. Moreover, this study also describes some security requirements to limit threats and also some standards of cloud

Model-driven interoperability: engineering heterogeneous IoT systems

Interoperability remains a significant burden to the developers of Internet of Things systems. This is because resources and APIs are dynamically composed; they are highly heterogeneous in terms of their underlying communication technologies, protocols and data formats, and interoperability tools remain limited to enforcing standards-based approaches. In this paper, we propose model-based engineering methods to reduce the development effort towards ensuring that complex software systems interoperate with one another. Lightweight interoperability models can be specified in order to monitor and test the execution of running software so that interoperability problems can be quickly identified, and solutions put in place. A graphical model editor and testing tool are also presented to highlight how a visual model improves upon textual specifications. We show using case-studies from the FIWARE Future Internet Service domain that the software framework can support non-expert developers to address interoperability challenges

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities