Towards a Traffic-aware Cloud-native Cellular Core

Advances in virtualization technologies have revolutionized the design of the core of cellular networks. However, the adoption of microservice design patterns and migration of services from purpose-built hardware to virtualized hardware has adversely affected the delivery of latency-sensitive services.

In this dissertation, we make a case for cloud-native (microservice container packaged) network functions in the cellular core by proposing domain knowledge-driven, traffic-aware, orchestration frameworks to make network placement decisions. We begin by evaluating the suitability of virtualization technologies for the cellular core and demonstrating that container-driven deployments can significantly outperform other virtualization technologies such as Virtual Machines for control and data plane applications.

To support the deployment of latency-sensitive applications on virtualized hardware, we propose using Virtual Network Function (VNF) bundles (aggregates) to handle transactions. Specifically, we design Invenio to leverage a combination of network traces and domain knowledge to identify VNFs involved in processing a specific transaction, which are then collocated by a traffic-aware orchestrator. By ensuring that a user request is processed by a single aggregate of collocated VNFs, Invenio can significantly reduce end-to-end latencies and improve user experience.

Finally, to understand the challenges in using container-driven deployments in real-world applications, we develop and evaluate a novel caller-ID spoofing detection solution in Voice over LTE (VoLTE) calls. Our proposed solution, NASCENT, cross validates the caller-ID used during voice-call signaling with a previously authenticated caller-ID to detect caller-ID spoofing. Our evaluation with traditional and container-driven deployments shows that container-driven deployment can not only support complex cellular services but also outperform traditional deployments.