Rule-Based Translation of Application-Level QoS Constraints into SDN Configurations for the IoT

Abstract

In this paper, we propose an approach for the automated translation of application-level requirements regarding the logical workflow and its QoS into a configuration of the underlying network substrate. Our goal is to facilitate the integration of QoS constraints in the development of industrial IoT applications to make them more reliable. We follow an approach based on two semantic models: The first model allows to design the workflow of an IoT application and to express applicationlevel QoS requirements on its interactions. The second model captures the configuration of a network and can be used as input to a north-bound interface of an SDN controller. Finally, we make use of rule-based semantic reasoning to automatically translate from the application requirements into SDN parameters.

Networking-Aware IoT Application Development

“…Based on the above outline model, we have implemented application-level QoS constraints on a semantics-based platform in our previous work [9]. Application-level QoS constraints refer to the possibility of defining such constraints on a high-level, independent of network-level specifics.…”

“…Using application-level constraints, we can ensure the availability of bandwidth from application development onwards. For more information on the possible implementation of such constraints via a semantic reasoner, see [9].…”

“…This work describes the latest findings on our research agenda to enable IoT applications as distributed choreographies. Our agenda started with the definition of the Recipe concept as template for IoT applications [6], continued by our approach for runtime management of such recipes [7], the dynamic and resilient management of IoT applications [8], and rule-based configuration of networks underlying the IoT application [9]. This work combines the results of these previous works and goes beyond by investigating, implementing, and evaluating an approach that automatically translates application-specific SPDI and QoS constraints into executable patterns/rules as well as their actual monitoring and enforcement.…”

Optimally Self-Healing IoT Choreographies

“…In this example, video and audio streams are connected to analytics components that feed into an aggregating intrusion detector and nally a component that is able to send noti cations. e recipe designer can further specify application-level constraints (e.g., minimum video frame rate) on the interactions between sensors and analysis services [25].…”

“…One possible storage mechanism for such data would be a semantic triple store such as Apache Jena 6 . With this semantic store, the system can take advantage of semantic reasoning and translation, as described in [25]. e con gurator controls the creation of the system and is responsible for con guring devices into a choreography.…”