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

Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges

Heterogeneous Vehicular NETworks (HetVNETs) can meet various quality-of-service (QoS) requirements for intelligent transport system (ITS) services by integrating different access networks coherently. However, the current network architecture for HetVNET cannot efficiently deal with the increasing demands of rapidly changing network landscape. Thanks to the centralization and flexibility of the cloud radio access network (Cloud-RAN), soft-defined networking (SDN) can conveniently be applied to support the dynamic nature of future HetVNET functions and various applications while reducing the operating costs. In this paper, we first propose the multi-layer Cloud RAN architecture for implementing the new network, where the multi-domain resources can be exploited as needed for vehicle users. Then, the high-level design of soft-defined HetVNET is presented in detail. Finally, we briefly discuss key challenges and solutions for this new network, corroborating its feasibility in the emerging fifth-generation (5G) era

Developing Iot-Based Geophysical Micro-Observatories Utilizing Cloud Computing

Instrumentation for collecting geophysical data, specifically heat flow, in lake and marine environments has been in existence for over fifty years. Despite this, the costs associated with data collection and the technological limitations of existing instrumentation can be preventative when conducting geophysical studies. Furthermore, the success rate of such studies is limited by the lack of real time data transmission capabilities when instruments are deployed for extended periods of time. As a solution to this problem we have created cost-effective and lightweight IoT-driven instrumentation and combined it with cloud computing technology facilitating real time data transmission to the cloud. Furthermore, we have also explored a new data analysis technique employing Granger causality-based time series clustering to investigate relationships between atmospheric data and water column temperature data