Understanding mobile network quality and infrastructure with user-side measurements

Measurement collection is a primary step towards analyzing and optimizing performance of a telecommunication service. With an Mobile Broadband (MBB) network, the measurement process has not only to track the network’s Quality of Service (QoS) features but also to asses a user’s perspective about its service performance. The later requirement leads to “user-side measurements” which assist in discovery of performance issues that makes a user of a service unsatisfied and finally switch to another network. User-side measurements also serve as first-hand survey of the problem domain. In this thesis, we exhibit the potential in the measurements collected at network edge by considering two well-known approaches namely crowdsourced and distributed testbed-based measurements. Primary focus is on exploiting crowdsourced measurements while dealing with the challenges associated with it. These challenges consist of differences in sampling densities at different parts of the region, skewed and non-uniform measurement layouts, inaccuracy in sampling locations, differences in RSS readings due to device-diversity and other non-ideal measurement sampling characteristics. In presence of heterogeneous characteristics of the user-side measurements we propose how to accurately detect mobile coverage holes, to devise sample selection process so to generate a reliable radio map with reduced sample cost, and to identify cellular infrastructure at places where the information is not public. Finally, the thesis unveils potential of a distributed measurement test-bed in retrieving performance features from domains including user’s context, service content and network features, and understanding impact from these features upon the MBB service at the application layer. By taking web-browsing as a case study, it further presents an objective web-browsing Quality of Experience (QoE) model

Modeling Variation in Mobile Download Speed in Presence of Missing Samples

A stably fast mobile broadband connectivity is key to customer retention. Mobile networks, however, suffer unpredictability in performance. Analyzing variability in network speed is, therefore, challenging since it tends to exhibit patterns at several time scales. Additionally, frequently monitoring it over time, is costly. In this article, we analyze speed measurements from 78 stationary probes, spread across Norway. Monitoring was performed thrice per day across the year, to assess performance of the two largest network operators. Despite being unique, the dataset involves a non-trivial extent of missing data. This study investigates the effect of missing data on the extracted performance patterns. We capture patterns with tensor factorizations, that show that missing data at random has a minimal effect on the identified patterns, and that depending upon the determinism of an operator’s performance, the acceptable size and structure of missing data varies. Our analysis shows that, for a probe, the difference in speed variation between real and imputed speed values can be around 7% for up to 40% missing data. We also identify that congestion, routine maintenance and sub-optimal network configuration cause high speed variability. These findings can help operators improving their offerings and deciding on optimal performance monitoring frequency

Evaluating the Cloud-RAN architecture: functional splitting and switched Ethernet Xhaul

The Cloud-RAN architecture is a key enabler to building future mobile networks in a flexible and cost-efficient way. For instance, switched Ethernet is a prime candidate for mobile transport networks (Xhaul), due to its flexibility, ubiquity, and cost-effectiveness. Understanding its performance under different network configurations would allow concluding about its appeal for Cloud-RAN. On the other hand, evaluating resource sharing mechanisms is relevant to put in place best solutions to host multiple virtual Base Band Units (vBBUs) into the same compute infrastructure. This paper assesses the feasibility of using a switched Ethernet Xhaul, by instantiating two BBUsusing different functional splits. Moreover, this paper evaluates two mec general purpose server (GPS) hosting vBBUs. Our results point to a marginal performance degradation caused by the switched Ethernet Xhaul and the NIC sharing mechanisms. Such deviations could be seen from the increase in average and maximum Jitter and RTT results

Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources

Cloud-based Radio Access Network (Cloud-RAN) leverages virtualization to enable the coexistence of multiple virtual Base Band Units (vBBUs) with collocated workloads on a single edge computer, aiming for economic and operational efficiency. However, this coexistence can cause performance degradation in vBBUs due to resource contention. In this paper, we conduct an empirical analysis of vBBU performance on a Linux RT-Kernel, highlighting the impact of resource sharing with user-space tasks and Kernel threads. Furthermore, we evaluate CPU management strategies such as CPU affinity and CPU isolation as potential solutions to these performance challenges. Our results highlight that the implementation of CPU affinity can significantly reduce throughput variability by up to 40%, decrease vBBU’s NACK ratios, and reduce vBBU scheduling latency within the Linux RT-Kernel. Collectively, these findings underscore the potential of CPU management strategies to enhance vBBU performance in Cloud-RAN environments, enabling more efficient and stable network operations. The paper concludes with a discussion on the efficient realization of Cloud-RAN, elucidating the benefits of implementing proposed CPU affinity allocations. The demonstrated enhancements, including reduced scheduling latency and improved end-to-end throughput, affirm the practicality and efficacy of the proposed strategies for optimizing Cloud-RAN deployments

PRINCIPIA: Opportunistic CPU and CPU-shares Allocation for Containerized Virtualization in Mobile Edge Computing

Leveraging virtualization technology, Mobile Edge Computing (MEC) deploys multiple services with different execution time requirements running as isolated processes. For instance, both real-time (RT) and non-RT applications may be (are) running on the same infrastructure using containerized virtualization. Nevertheless, sharing resources (e.g., CPU) with collocated workloads could impact the RT performance of RT applications. This paper presents PRINCIPIA, a dynamic CPU and CPU-shares allocation mechanism that opportunistically enables non-RT applications to run on underutilized CPUs while providing RT guarantees to RT applications. By monitoring MEC’s system metrics like processor’s CPU utilization and container’s CPU usage, PRINCIPIA dynamically allocates both CPU and CPU-shares to containers running non-RT applications aiming at opportunistically exploiting underutilized CPUs by containers running RT applications. We evaluate PRINCIPIA on a small-scale MEC server which uses containerized virtualization along with Linux RT Kernel to deploy both RT and non-RT applications. Our findings show that PRINCIPIA mitigates the impact on the RT performance of RT applications providing bounded processing latency in comparison with the default host Kernel scheduler

Opportunistic CPU Sharing in Mobile Edge Computing Deploying the Cloud-RAN

Leveraging virtualization technology, Cloud-RAN deploys multiple virtual Base Band Units (vBBUs) along with collocated applications on the same Mobile Edge Computing (MEC) server. However, the performance of real-time (RT) applications such as the vBBU could potentially be impacted by sharing computing resources with collocated workloads. To address this challenge, this paper presents a dynamic CPU sharing mechanism, specifically designed for containerized virtualization in MEC servers, that hosts both RT and non-RT general-purpose applications. Initially, the CPU sharing problem in MEC servers is formulated as a Mixed-Integer Programming (MIP). Then, we present an algorithmic solution that breaks down the MIP into simpler subproblems that are then solved using efficient, constant factor heuristics. We assessed the performance of this mechanism against instances of a commercial solver. Further, via a small-scale testbed, we assessed various CPU sharing mechanisms and their effectiveness in reducing the impact of CPU sharing indicate that our CPU sharing mechanism reduces the worstcase execution time by more than 150% compared to the default host RT-Kernel approach. This evidence is strengthened when evaluating this mechanism within Cloud-RAN, in which vBBUs share resources with collocated applications on a MEC server. Using our CPU sharing approach, the vBBU’s scheduling latency decreases by up to 21% in comparison with the host RT-Kernel

Bottleneck Identification in Cloudified Mobile Networks Based on Distributed Telemetry

Cloudified mobile networks are expected to deliver a multitude of services with reduced capital and operating expenses. A characteristic example is 5G networks serving several slices in parallel. Such mobile networks, therefore, need to ensure that the SLAs of customised end-to-end sliced services are met. This requires monitoring the resource usage and characteristics of data flows at the virtualised network core, as well as tracking the performance of the radio interfaces and UEs. A centralised monitoring architecture can not scale to support millions of UEs though. This paper, proposes a 2-stage distributed telemetry framework in which UEs act as early warning sensors. After UEs flag an anomaly, a ML model is activated, at network controller, to attribute the cause of the anomaly. The framework achieves 85% F1-score in detecting anomalies caused by different bottlenecks, and an overall 89% F1-score in attributing these bottlenecks. This accuracy of our distributed framework is similar to that of a centralised monitoring system, but with no overhead of transmitting UE-based telemetry data to the centralised controller. The study also finds that passive in-band network telemetry has the potential to replace active monitoring and can further reduce the overhead of a network monitoring system