Breaking the Barriers: Abstracting Traffic Management for Superior Quality of Experience in Multi-Technology Networks (2023)

By Sebnem Ozer, Ph.D., Charter Communications; Ramneek Bali, Charter Communications; Kamran Yousuf, Charter Communications; Moutaz Elkaissi, Charter Communications

Today, cable service providers deploy a variety of network technologies to offer a wider range of services to their customers and to tailor their services to specific geographic areas or customer needs. These technologies include Hybrid Fiber-Coaxial (HFC), Passive Optical Networks (PON) and Mobile Networks, with WiFi as the final leg of the connection to end devices or as a hotspot network. Deploying different network technologies can be challenging due to the increased complexity and cost of managing multiple networks. Operators may also face interoperability issues, which can result in poor service quality or higher maintenance costs. Distinct standards govern each of these technologies, which are further characterized by disparate network and service components. Consequently, the development of Quality of Experience (QoE) platforms becomes inherently tailored to specific technologies, creating barriers to achieving a cohesive and unified service framework. Within this paper, we present a compelling use case for Internet Engineering Task Force - Low Latency, Low Loss, and Scalable Throughput (IETF L4S) and 5G midhaul support [1] over DOCSIS networks in order to optimize the customer’s QoE for emerging and future use cases. Our focus delves into the exploration and evaluation of the mapping of 5G Stream Control Transmission Protocol (SCTP) signaling traffic onto the low latency Data Over Cable Service Interface Specification (LL DOCSIS/LLD Service Flow, substantiating the capacity of MSOs to accommodate this emerging service with the LLD support. Furthermore, we address the support for the IETF L4S traffic over multiple network segments. The study encompasses crucial facets like traffic classification, resource mapping and monitoring metrics, each pertaining to the corresponding network segments. The LLD test system described in this paper uses manual processes for traffic mapping and key performance indicator (KPI) monitoring in a controlled lab environment. Building upon this foundation, we propose an automated solution to support end-to-end QoE by abstracting traffic management with a high-level orchestration platform and a digitized and hierarchical component structure. Ultimately, our paper provides guidelines to ensure optimal QoE delivery while simultaneously curbing the complexities and costs associated with traffic management.

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