Micro-services dominate tomorrows virtualized network infrastructures and are going to abolish monolithic software. Paradigms like fog computing and the Internet of Things require virtualized packages of software to be distributed all over the network to form a service chain that enhances the overall Quality of Service. Therefore, SDN controllers need to be extendable, flexible, updatable, and manageable, which necessitates automation in the controller's application development and deployment. Such novel agile service management up-rose due to the increasing demand of micro-service structures in data centers and nowadays falls into the scope of DevOps activities. In our paper, we build a sample application for ONOS and deploy it within a virtualized environment to different infrastructures. Additionally, we provide an evaluation of different Continuous Integration (CI) tools for building and deploying our ONOS controller application. Our investigation shows the applicability of current CI solutions regarding an automated build, test, and deploy chain for SDN controller applications.

SensIoT is an open-source sensor monitoring framework for the Internet of Things, which utilizes proven technologies to enable easy deployment and maintenance while staying flexible and scalable. It closes the gap between highly specialized and, therefore, inflexible sensor monitoring solutions, which are only adjusted to a specific context, and the development of every other solution from scratch. Our framework fits a variety of use cases by providing an easy to set up, extensible, and affordable solution. The development is based on our former published framework MonTreAL, whose goal is to offer an environmental monitoring solution for libraries to guarantee cultural heritage to be conserved and prevented from serious damage, for example, from mold formation in closed stocks. It is a solution with virtualized microservices delivered by a famous container technology called Docker that is solely executable on one or more single board computers like the Raspberry Pi by providing automatic scaling and resilience of all sensor services. For SensIoT we extended the capability of MonTreAL to integrate commodity servers into the cluster to enhance the ease of setup and maintainability on already existing infrastructures. Therefore, we followed the paradigm to distribute microservices on small computing nodes first, thus not utilizing well-known cloud computing concepts. To achieve resilience and fault tolerance we also based our system on a microservice architecture, where the service orchestration is solved by Docker Swarm. As proof of concept, we are able to present our current data collection of the University of Bamberg’s Library that runs our system since autumn 2017. To make our system even better we are working on the integration of other sensor types and better performance management of SD-cards in Raspberry Pis.

The proliferation of smart devices for mobile networks is a major traffic generator nowadays. These devices provide the ability to receive media content in nearly every situation. Despite that video streaming in high quality is getting more and more popular in mobile scenarios, the performance and bottlenecks of mobile applications over wireless networks, especially, during the transmission of media streams, are poorly understood yet. In order to tackle this new challenge, we present an Android based framework to capture the relevant wireless network behavior, geo-coordinates and packet traces for popular streaming applications on Android certified devices. A dataset has been obtained by measurement trials, which have been performed in a 3G network for both HTTP and peer-to-peer video streaming applications. The trials comprise also an additional WiFi measurement for comparison purposes. The presented dataset enables future research to determine the quality of service and network characteristics of different streaming methodologies, which are affected by the typical conditions encountered in wireless networks, like hand-over effects, signal fading, connection losses etc. We hope that both, the presented dataset and the framework, may prove to be useful for the traffic measurement and the multimedia research communities.

Mobile video consumption establishes as a massively growing market. To guarantee an acceptable quality of experience to the end user, current studies try to improve data transmissions on air interfaces. However, the bottleneck in cellular mobile networks is the traversal of the infrastructure. A paradigm to circumvent this fact and to increase the system's capacity is given by device-to-device communication in combination with proximity detection. Those networks promise scalability and performance improvements in utilizing scarce resources. This principle is easy to integrate into an existing mobile peer-to-peer video streaming system. In this study, we present measurements on application level to determine the performance that a smart device contributes to different wireless networks. The results outline that content uploading from smart devices is negligible in cellular networks, but shows acceptable performance in WiFi networks. This evaluation is the foundation to build a proximity enhanced video streaming system for mobile devices.