Design of Secure Scalable Frameworks for Next Generation Cellular Networks

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Virginia Tech


Leveraging Network Functions Virtualization (NFV), the Fifth Generation (5G) core, and Radio Access Network (RAN) functions are implemented as Virtual Network Functions (VNFs) on Commercial-off-the-Shelf (COTS) hardware. The use of virtualized micro-services to implement these 5G VNFs enables the flexible and scalable construction of end-to-end logically isolated network fragments denoted as network slices.

The goal of this dissertation is to design more scalable, flexible, secure, and visible 5G networks. Thus, each chapter will present a design and evaluation that addresses one or more of these aspects.

The first objective is to understand the limits of 5G core micro-service virtualization when using lightweight containers for constructing various network slicing models with different service guarantees. The initial deployment model consists of the OpenAirInterface (OAI) 5G core in a containerized setting to create a universally deployable testbed. Operational and computational stress tests are performed on individual 5G core VNFs where different network slicing models are created that are applicable to real-life scenarios. The analysis captures the increase in compute resource consumption of individual VNFs during various core network procedures. Furthermore, using different network slicing models, the progressive increase in resource consumption can be seen as the service guarantees of the slices become more demanding. The framework created using this testbed is the first to provide such analytics on lightweight virtualized 5G core VNFs with large-scale end-to-end connections.

Moving into the cloud-native ecosystem, 5G core deployments will be orchestrated by middle-men Network-slice-as-a-Service (NSaaS) providers. These NSaaS providers will consume Infrastructure-as-a-service (IaaS) offerings and offer network slices to Mobile Virtual Network Operators (MVNOs). To investigate this future model, end-to-end emulated 5G deployments are conducted to offer insight into the cost implications surrounding such NSaaS offerings in the cloud. The deployment features real-life traffic patterns corresponding to practical use cases which are matched with specific network slicing models. These models are implemented in a 5G testbed to gather compute resource consumption metrics. The obtained data are used to formulate infrastructure procurement costs for popular cloud providers such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. The results show steady patterns in compute consumption across multiple use cases, which are used to make high-scale cost projections for public cloud deployments. In the end, the trade-off between cost and throughput is achieved by decentralizing the network slices and offloading the user plane.

The next step is the demystification of 5G traffic patterns using the Over-the-Air (OTA) testbed. An open-source OTA testbed is constructed leveraging advanced features of 5G radio access and core networks developed by OAI. The achievable Quality of Service (QoS) is evaluated to provide visibility into the compute consumption of individual components. Additionally, a method is presented to utilize WiFi devices for experimenting with 5G QoS. Resource consumption analytics are collected from the 5G user plane in correlation to raw traffic patterns. The results show that the open-source 5G testbed can sustain sub-20ms latency with up to 80Mbps throughput over a 25m range using COTS devices. Device connection remains stable while supporting different use cases such as AR/VR, online gaming, video streaming, and Voice-over IP (VoIP). It illustrates how these popular use cases affect CPU utilization in the user plane. This provides insight into the capabilities of existing 5G solutions by demystifying the resource needs of specific use cases.

Moving into public cloud-based deployments, creates a growing demand for general-purpose compute resources as 5G deployments continue to expand. Given their existing infrastructures, cloud providers such as AWS are attractive platforms to address this need. Therefore, it is crucial to understand the control and user plane QoS implications associated with deploying the 5G core on top of AWS. To this end, a 5G testbed is constructed using open-source components spanning multiple global locations within the AWS infrastructure. Using different core deployment strategies by shuffling VNFs into AWS edge zones, an operational breakdown of the latency overhead is conducted for 5G procedures. The results show that moving specific VNFs into edge regions reduces the latency overhead for key 5G operations. Multiple user plane connections are instantiated between availability zones and edge regions with different traffic loads. As more data sessions are instantiated, it is observed that the deterioration of connection quality varies depending on traffic load. Ultimately, the findings provide new insights for MVNOs to determine favorable placements of their 5G core entities in the cloud.

The transition into cloud-native deployments has encouraged the development of supportive platforms for 5G. One such framework is the OpenRAN initiative, led by the O-RAN Alliance. The OpenRAN initiative promotes an open Radio Access Network (RAN) and offers operators fine-grained control over the radio stack. To that end, O-RAN introduces new components to the 5G ecosystem, such as the near real-time RAN Intelligent Controller (near-RT RIC) and the accompanying Extensible Applications (xApps). The introduction of these entities expands the 5G threat surface. Furthermore, with the movement from proprietary hardware to virtual environments enabled by NFV, attack vectors that exploit the existing NFV attack surface pose additional threats. To deal with these threats, the textbf{xApp repository function (XRF)} framework is constructed for scalable authentication, authorization, and discovery of xApps. In order to harden the XRF microservices, deployments are isolated using Intel Software Guard Extensions (SGX). The XRF modules are individually benchmarked to compare how different microservices behave in terms of computational overhead when deployed in virtual and hardware-based isolation sandboxes. The evaluation shows that the XRF framework scales efficiently in a multi-threaded Kubernetes environment. Isolation of the XRF microservices introduces different amounts of processing overhead depending on the sandboxing strategy. A security analysis is conducted to show how the XRF framework addresses chosen key issues from the O-RAN and 5G standardization efforts.

In the final chapter of the dissertation, the focus shifts towards the development and evaluation of 5G-STREAM, a service mesh tailored for rapid, efficient, and authorized microservices in cloud-based 5G core networks. 5G-STREAM addresses critical scalability and efficiency challenges in the 5G core control plane by optimizing traffic and reducing signaling congestion across distributed cloud environments. The framework enhances Virtual Network Function (VNF) service chains' topology awareness, enabling dynamic configuration of communication pathways which significantly reduces discovery and authorization signaling overhead. A prototype of 5G-STREAM was developed and tested, showing a reduction of up to 2× in inter-VNF latency per HTTP transaction in the core network service chains, particularly benefiting larger service chains with extensive messaging. Additionally, 5G-STREAM's deployment strategies for VNF placement are explored to further optimize performance and cost efficiency in cloud-based infrastructures, ultimately providing a scalable solution that can adapt to increasing network demands while maintaining robust service levels. This innovative approach signifies a pivotal advancement in managing 5G core networks, paving the way for more dynamic, efficient, and cost-effective cellular network infrastructures.

Overall, this dissertation is devoted to designing, building, and evaluating scalable and secure 5G deployments.



Next-G, Scalability, Security, Cloud, Microservices