# Modeling and Profiling of Aggregated Industrial Network Traffic
## 1 Introduction  
The concept of Industrial IoT encompasses the joint applicability of operation, internet, and information technologies to expand the efficiency expectation of automation to green and flexible processes with innovative products and services. A requirement for ensuing this integration is the transformation of industrial network infrastructures to enable the accommodation of new traffic from different technologies. This transformation is step-wise and needs many considerations to ensure the successful development of future industrial networks. One essential consideration is to ensure the continuous operation of the existing system, the machinery, and infrastructure also known as brownfield installation, to avoid risk of downtime. The importance and benefits of consolidated networks have been discussed from various aspects, and their challenges have been addressed from different technical perspectives [1; 2; 3]. One dominant challenge for industrial networks to overcome is satisfying the diverse and, in some cases, contradictory requirements of the Internet technology (IT) and operational technology (OT) systems, like real-time performance and high throughput. Time-Sensitive Networking (TSN) [4] provides a toolbox to provide mechanisms for any possible traffic type that are predicted to coexist in the future industrial networks. The Orchestration of various applications in industrial ecosystems, with heterogeneous industrial communication protocols such as Open Platform Communication (OPC), MTConnect and message queue telemetry transport (MQTT), raises as a significant obstacle for system integration [5]. The works more concerned with the interoperability of different systems, by providing a communication middleware satisfying control systems requirements, are mostly presented in Open Platform Communications Unified Architecture (OPC-UA) [5; 6]. The future industrial networks pose demanding requirements, and in some cases unpredictable challenges, on network infrastructure. One of the impacted domains by these challenges is network resource management, where integration of new technologies and applications into the existing networks translate to scaling-up communication and new configuration with no negative impacts on the performance of the ongoing processes. Recently, the academic and industrial communities started the conversation on the importance of studying brownfield installations and the need to support the legacy systems to guarantee the performance when transforming industrial networks [3; 7]. In [3] the challenges in automation for future industrial networks are detailed. It is concluded that additional insights from the traffic of existing installation are prerequisites for the integration of new technologies and providing the intermediate steps required for the evolutionary transformation of the industrial automation networks. The additional insight from the state of ongoing processes can identify the current resource plan of the ongoing network communication in terms of bandwidth, dynamic throughput, delivery times, and scheduled traffics. This insight can contribute to the development of integration strategies that ensure adequate outcomes based on predefined performance metrics in the complex integrated networks for network management tasks such as provisioning and dimensioning [8; 9; 10]. In other words, resource management with respect to the new integration characteristic and required resources, while safeguarding the performance of legacy systems and ongoing processes. Hereof, a clear view of the operational state of the existing network is an undeniable prerequisite. However, this prerequisite is not a trivial goal to achieve due to the unavailability of data from industrial plants and the complexity of their traffic. The unavailability of data from industrial plants is a major factor limiting the application of promising methods for developing solutions that address the existing and foreseeable future challenges for network management.