As the primary channel for users to report and resolve service issues, customer care has historically been a critical and resource-intensive aspect of operating cellular networks. However, owing to the inherent complexity in correlating network events with the service performance experienced by individual users, adoption of data-driven solutions leveraging network data for realtime troubleshooting during customer care calls has remained a challenge to cellular service providers (CSPs). In this work, we propose a novel ML aSsisted cEllular sErvice diagNosis (SEEN) solution that infers the cause of user service issues from performance metrics observed from network and assists care agents during customer care calls. Our extensive evaluations demonstrated that SEEN can accurately identify the root cause of user reported performance issues in >80% cases, without relying on information provided by users. Accurate root cause prediction coupled with automated recommended resolution actions implemented in SEEN, lead to significant reduction in handling time to resolve service issues and in trouble tickets volume, improving customer satisfaction and reducing customer care operational expense. Benefit of SEEN is further demonstrated by field deployment in a large CSP.

We propose an optimization to reduce the latency incurred by a stateless 5G control plane. The key idea is to avoid redundant database read operations. We achieve this by reading the user’s state only once and sending it to successive network functions in a chain. Experimental results show that this optimization can reduce the total cost by 33% on average.

One of the key challenges faced by cellular network customer care agents is identifying if the service problem is caused by network-related issues or user-device-related issues. Some service providers [5], [6], therefore, employ machine learning-based troubleshooting frameworks to aid care agents in identifying the root cause of service problems experienced by users. However, obtaining large-scale and comprehensive ground truth troubleshooting result data is costly and requires tremendous manual efforts from networking operators. Due to this limitation, training such a machine learning (ML) model is rather challenging as the model can easily overfit to the limited available ground truth data. In this work, we propose a novel two-stage learning framework to improve the classification accuracy of ML-based troubleshooting frameworks. Our proposed framework uses resolution action taken by the care agent coupled with network/device data collected after the care call to infer accurate ground truth which is then used to train classification models.

The adoption of a cloud-native architecture in 5G networks has facilitated rapid deployment and update of cellular services. An important part of this architecture is the implementation of 5G network functions statelessly. However, statelessness and its associated serialization and de-serialization of data and database interaction significantly increase latency. In this work, we take the first steps towards quantifying the cost of statelessness in a cloud-native 5G system. We compare the cost of different state management paradigms, and propose a number of optimizations to reduce this cost. Our preliminary results indicate that sharing user state among 5G functions reduces the overall cost by on an average of 10% in experiments with 100 to 1000 simultaneous requests. Optimizations such as non-blocking calls and custom database APIs also reduce cost, albeit to a lower extent. We believe that the paradigms proposed in this paper can aid operators and software vendors as they design cloud-native 5G networks.

Microservices enable rapid service deployment and scaling. Integrating poorly-understood microservice components into Service Function Chains (SFCs) or graphs limits a provider's control over service delivery latency, however. Orchestration frameworks currently instantiate and place myriads of microservice components without knowing the impact of placement decisions on latency.

We propose CoRE, a 360° video streaming approach that reduces bandwidth requirements compared to transferring the entire 360° video. CoRE uses non-linear sampling in both the spatial and temporal domains to achieve robustness to view direction prediction error and to transient wireless network bandwidth fluctuation. Each CoRE frame samples the environment in all directions, with full resolution over the predicted field of view and gradually decreasing resolution at the periphery, so that missing pixels are avoided, irrespective of the view prediction error magnitude. A CoRE video chunk has a main part at full frame rate, and an extension part at a gradually decreasing frame rate, which avoids stalls while waiting for a delayed transfer. We evaluate a prototype implementation of CoRE through trace-based experiments and a user study, and find that, compared to tiling with low-resolution padding, CoRE reduces data transfer amounts, stalls, and H.264 decoding overhead, increases frame rates, and eliminates missing pixels.

Cellular service carriers often employ reactive strategies to assist customers who experience non-outage related individual service degradation issues (e.g., service performance degradations that do not impact customers at scale and are likely caused by network provisioning issues for individual devices). Customers need to contact customer care to request assistance before these issues are resolved. This paper presents our experience with PACE (ProActive customer CarE), a novel, proactive system that monitors, troubleshoots and resolves individual service issues, without having to rely on customers to first contact customer care for assistance. PACE seeks to improve customer experience and care operation efficiency by automatically detecting individual (non-outage related) service issues, prioritizing repair actions by predicting customers who are likely to contact care to report their issues, and proactively triggering actions to resolve these issues. We develop three machine learning-based prediction models, and implement a fully automated system that integrates these prediction models and takes resolution actions for individual customers. We conduct a large-scale trace-driven evaluation using real-world data collected from a major cellular carrier in the US, and demonstrate that PACE is able to predict customers who are likely to contact care due to non-outage related individual service issues with high accuracy. We further deploy PACE into this cellular carrier network. Our field trial results show that PACE is effective in proactively resolving non-outage related individual customer service issues, improving customer experience, and reducing the need for customers to report their service issues.

CoRE is an approach for streaming 360° videos based on a non-linear sampling of the equirectangular video cube. CoRE is robust to view prediction errors.

Caller-ID spoofing deceives the callee into believing a call is originating from another user. Spoofing has been strategically used in the now-pervasive telephone fraud, causing substantial monetary loss and sensitive data leakage. Unfortunately, caller-ID spoofing is feasible even when user authentication is in place. State-of-the-art solutions either exhibit high overhead or require extensive upgrades, and thus are unlikely to be deployed in the near future. In this paper, we seek an effective and efficient solution for 4G (and conceptually 5G) carrier networks to detect (and block) caller-ID spoofing. Specifically, we propose Nascent, Network-assisted caller ID authentication, to validate the caller-ID used during call setup which may not match the previously-authenticated ID. Nascent functionality is split between data-plane gateways and call control session functions. By leveraging existing communication interfaces between the two and authentication data already available at the gateways, Nascent only requires small, standard-compatible patches to the existing 4G infrastructure. We prototype and experimentally evaluate three variants of Nascent in traditional and Network Functions Virtualization (NFV) deployments. We demonstrate that Nascent significantly reduces overhead compared to the state-of-the-art, without sacrificing effectiveness.