استكشف المجموعة الواسعة من العناوين المتاحة.

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.

Clustering is an extraction of closely knitted groups from a set of nodes. Its benefits in social network range from applying marketing schemes on an appropriate interest group to social network analysis. It is also considered an important tool for efficient communication in an intermittent Pocket Switched Network (PSN). Contact probability between mobile devices in disrupted social networks greatly depends upon the mobility profile and level of relationships between the device holders. Unlike flat routing, scalable and efficient routing in these networks is highly dependent upon accurate derivation of social circles or clusters. This paper therefore evaluates existing clustering techniques for terrestrial social network with the end aim of minimizing communication overhead by identifying those message carriers that can bring message closer to destination node. In order to ensure intercluster routing, modification in existing schemes is proposed so as to detect bridge nodes between single hop destination clusters and to find path towards a disjoint destination cluster.

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.

The rapid spread of the novel corona virus, SARS-CoV-2, has prompted an unprecedented response from governments across the world. A third of the world population have been placed in varying degrees of lockdown, and the Internet has become the primary medium for conducting most businesses and schooling activities. This paper aims to provide a multi-prospective account of Internet performance during the first wave of the pandemic. We investigate the performance of the Internet control plane and data plane from a number of globally spread vantage points. We also look closer at two case studies. First, we look at growth in video traffic during the pandemic, using traffic logs from a global video conferencing provider. Second, we leverage a country-wide deployment of measurement probes to assess the performance of mobile networks during the outbreak. We find that the lockdown has visibly impacted almost all aspects of Internet performance. Access networks have experienced an increase in peak and off-peak end to end latency. Mobile networks exhibit significant changes in download speed, while certain types of video traffic has increased by an order of magnitude. Despite these changes, the Internet seems to have coped reasonably well with the lockdown traffic.