Enhancing SDN performance by enabling reasoning abilities in data traffic control

Abstract

Software-defined network (SDN) is becoming the most suitable solution to cover huge network development cost without concerning about their physical limitations. Applications working with SDN for data traffic control are familiar with their utilization in the system, but semantics are not part of the data model for reasoning to play a role. Furthermore, it clarifies the idea that current data models are not sufficient to cover recent trends toward data traffic monitoring requirements. On the other hand, Resource Description Framework (RDF) data model for Semantic Web (SW) has become a standard for data modeling and analysis. This data model has wider support for machine intelligence. A methodology becomes desirable so that the data can be used to track updates intact intelligently with the help of data information transformation. Such method can prove to be beneficial for systems forming broad and real-time distribution using SDN platform for networking. Therefore, such an approach can reduce the conceptual gap between intelligence and data monitoring for better throughput. This research attempts to provide a methodology for covering data mapping, data transformation, and change control for traffic control. It is resulting in forming a cooperative environment for SDN-based adaptation between system and application, which brings data traffic monitoring at entirely different scales.