This page is a placeholder for cataloging research projects that make use of Aether, with the goal of illustrating how Aether can be used in the lab to investigate and demonstrate new features. If you have a project that uses Aether, please add a summary/abstract of your work (along with links to any results or artifacts) to this page.
Project #1: Tegra: A Fast, Flexible, and Scalable Cloud-Native 5G Core
To support the rapidly evolving mobile use cases (e.g., AR/VR, autonomous driving, and massive IoT), the 5G mobile core (5GC) is being architected as a service-based architecture (SBA) workload running on private/public clouds. Yet, the current proposals to improve its performance still revert to old methods used in traditional NFV-based mobile core designs (e.g., consolidating functions on dedicated servers). It is critical to understand whether there is a fundamental tradeoff between achieving greater flexibility and higher performance in SBA-based mobile cores.
To answer this question, we conduct an in-depth study of a 5G-compliant open-source mobile core (i.e., Aether) to characterize its various bottlenecks. Our measurements show that, unlike NFV-based designs, the limited scalability of current SBA-based 5GC is not inherent to the disaggregated SBA; instead, it stems from (a) the limitations of current implementations (which lack parallelism in key steps of the pipelined execution of microservices) and (b) our oversight regarding the distinctive role these cores play within the network—managing bidirectional (uplink and downlink) events.
Based on these observations, we develop a fast, flexible, and scalable cloud-native 5G core, called Tegra, which proposes scalable and resilient microservices abstractions and state-management strategies to enable autoscaling, load balancing, and fault-tolerance in current SBA-based 5GC. Our evaluation shows that Tegra achieves significantly lower latencies—finishing requests 14× and 4× faster (on average) than Free5GC and Aether, respectively.
Team: Bilal Saleem, Omar Basit, Jiayi Meng, Jingqi Huang, Ajay Thakur, Iftekhar Alam, Christian Maciocco, Y. Charlie Hu, and Muhammad Shahbaz