Toward a Live BBU Container Migration in Wireless Networks

Cloud Radio Access Networks (Cloud-RANs) have recently emerged as a promising architecture to meet the increasing demands and expectations of future wireless networks. Such an architecture can enable dynamic and flexible network operations to address significant challenges, such as higher mobile traffic volumes and increasing network operation costs. However, the implementation of compute-intensive signal processing Network Functions (NFs) on the General Purpose Processors (General Purpose Processors) that are typically found in data centers could lead to performance complications, such as in the case of overloaded servers. There is therefore a need for methods that ensure the availability and continuity of critical wireless network functionality in such circumstances. Motivated by the goal of providing highly available and fault-tolerant functionality in Cloud-RAN-based networks, this paper proposes the design, specification, and implementation of live migration of containerized Baseband Units (BBUs) in two wireless network settings, namely Long Range Wide Area Network (LoRaWAN) and Long Term Evolution (LTE) networks. Driven by the requirements and critical challenges of live migration, the approach shows that in the case of LoRaWAN networks, the migration of BBUs is currently possible with relatively low downtimes to support network continuity. The analysis and comparison of the performance of functional splits and cell configurations in both networks were performed in terms of fronthaul throughput requirements. The results obtained from such an analysis can be used by both service providers and network operators in the deployment and optimization of Cloud-RANs services, in order to ensure network reliability and continuity in cloud environments

An Online Multi-dimensional Knapsack Approach for Slice Admission Control

Network Slicing has emerged as a powerful technique to enable cost-effective, multi-tenant communications and services over a shared physical mobile network infrastructure. One major challenge of service provisioning in slice-enabled networks is the uncertainty in the demand for the limited network resources that must be shared among existing slices and potentially new Network Slice Requests. In this paper, we consider admission control of Network Slice Requests in an online setting, with the goal of maximizing the long-term revenue received from admitted requests. We model the Slice Admission Control problem as an Online Multidimensional Knapsack Problem and present two reservation-based policies and their algorithms, which have a competitive performance for Online Multidimensional Knapsack Problems. Through Monte Carlo simulations, we evaluate the performance of our online admission control method in terms of average revenue gained by the Infrastructure Provider, system resource utilization, and the ratio of accepted slice requests. We compare our approach with those of the online First Come First Serve greedy policy. The simulation's results prove that our proposed online policies increase revenues for Infrastructure Providers by up to 12.9 % while reducing the average resource consumption by up to 1.7%. In particular, when the tenants' economic inequality increases, an Infrastructure Provider who adopts our proposed online admission policies gains higher revenues compared to an Infrastructure Provider who adopts First Come First Serve

Towards Personality Detection and Prediction using Smartphone Sensor Data

The emergence and adoption of mobile smartphones and their ubiquitous use by mobile phone users provide a distinctive opportunity for the collection of multi-modal sensor data. The data collected from such smartphone devices can be used to observe, recognize and predict patterns in user behaviors such as physical activity, psycho-physiological conditions such as affective state, personality traits such as extraversion, emotionality, and honesty-humility, as well as other mental health-related or psychological variables. In this work, we design and develop a system that can be used for the unobtrusive collection and storage of anonymized smartphone sensor data for personality detection and prediction. Our proposed system is developed across the two main smartphone operating systems to cover a wide range of users and to consider the available sensors across platform-dependent devices, as well as to account for distinctions in the personality traits of users of different operating systems. We present a correlation analysis and describe how the dataset collected from the smartphone sensors, which were deployed in a large-scale psychology study with about 200 participants, can be used to detect and predict different personality states