No abstract

Summary Software‐defined networking (SDN) is a new network paradigm that is separating the data plane and the control plane of the network, making one or more centralized controllers to supervise the behaviour of the entire network. Different types of SDN controller software exist, and research dealing with the difficulties of consistently integrating these different controller types has mostly been declared future work. In this paper, the Domino framework is proposed, a pluggable SDN framework for managing heterogeneous SDN networks. In contrast to related work, the proposed framework allows research into SDN networks controlled by different types of SDN controllers attempting to standardize the northbound API of them. Domino implements a microservice plugin architecture where users can link different SDN networks to a processing algorithm. Such an algorithm allows for, eg, adapting the flows by building a pipeline using plugins that either invoke other SDN operations or generic data processing algorithms. The Domino framework is evaluated by implementing a proof‐of‐concept implementation, which is tested on the initial requirements. It achieves the modifiability and the interoperability with an average successful exchange ratio of 99.99%. The performance requirements are met for the frequently used commands with an average response time of 0.26 seconds, and the framework can handle at least 72 plugins simultaneously depending on the available amount of RAM. The proposed framework is evaluated by means of the implementation of a shortest path routing algorithm between heterogeneous SDN networks.

SummaryEmergency services must be able to transfer data with high priority over different networks. With 5G, slicing concepts at mobile network connections are introduced, allowing operators to divide portions of their network for specific use cases. In addition, Software‐Defined Networking (SDN) principles allow to assign different Quality‐of‐Service (QoS) levels to different network slices.This paper proposes a microservices‐based framework, able to run both centralized and distributed, that guarantees the required bandwidth for the emergency flows and maximizes the best‐effort flows over the remaining bandwidth based on their priority. The proposed framework consists of an offline linear model, allowing to optimize the problem for a batch of flow requests. For dynamic situations, an online approach is also required in the framework to handle new incoming flows by calculating the path with a shortest path algorithm and utilizing a greedy approach in assigning bandwidth to the intermediate flows.In this article, the linear model is evaluated through simulation, the distributed architecture is evaluated through emulation while the online approach is validated through physical experiments with SDN switches. The results show that the linear model is able to guarantee the resource allocation for the emergency flows while optimizing the best‐effort flows with a sub‐second execution time. The distributed architecture is able to split up the managed network into different parts, allowing division of work between controllers. As a proof‐of‐concept, a prototype with Zodiac switches validates the feasibility of the centralized framework.

In case of a dangerous incident, such as a fire, a collision or an earthquake, a lot of contextual data is available for the first incident responders when handling this incident. Based on this data, a commander on scene or dispatchers need to make split-second decisions to get a good overview on the situation and to avoid further injuries or risks. Therefore, we propose a decision support system that can aid incident responders on scene in prioritizing the rescue efforts that need to be addressed. The system collects relevant data from a custom designed drone by detecting objects such as firefighters, fires, victims, fuel tanks, etc. The drone autonomously observes the incident area, and based on the detected information it proposes a prioritized based action list on e.g. urgency or danger to incident responders. This paper presents the architecture of the framework and a prototype implementation and evaluation of a decision support system, responsible for digesting and prioritizing the large amount of contextual data captured at an incident site. The evaluation of the decision support system shows that the proposed solution works accurately in supporting incident responders in providing a sorted overview of the actions needed in realtime, with an average response time of 334ms on a less powerful device and 263ms on a powerful device equipped with a GPU.

Nowadays, many complex multi-vendor production environments, such as telecom infrastructures in smart cities or on-board passenger information systems in trains, are based on micro-services and deployed in the cloud. From a service integrator viewpoint, building new solutions for these environments, which can host a large number of externally designed and developed micro-services, is often complex and error-prone. This is in part due to undocumented behaviour or undocumented architectural specifications of such systems. Advanced service monitoring can offer a solution to quickly detect anomalies or unexpected service interaction behaviour during on-site integration. However, the monitoring service should not have an impact on the production environment itself. Therefore, this article proposes an agent-based unobtrusive monitoring platform, capable of monitoring both internally developed and externally developed services through the use of sidecar containers. It monitors state, metrics and network traffic at micro-service level and the research was conducted as part of the DynAMo research project, a collaboration with various industry partners.Prototype evaluation proves that our solution has a negligible impact (below 0.02% CPU usage on average) on an existing micro-service environment just as other monitoring systems like Prometheus while offering additional functionality focused on multi-vendor service integration. This makes it suitable to be deployed in complex production domains to further aid on-site integration and quickly find potential new anomalies.