Design, implementation and experimental evaluation of a network-slicing aware mobile protocol stack

Mención Internacional en el título de doctorWith the arrival of new generation mobile networks, we currently observe a paradigm shift, where monolithic network functions running on dedicated hardware are now implemented as software pieces that can be virtualized on general purpose hardware platforms. This paradigm shift stands on the softwarization of network functions and the adoption of virtualization techniques. Network Function Virtualization (NFV) comprises softwarization of network elements and virtualization of these components. It brings multiple advantages: (i) Flexibility, allowing an easy management of the virtual network functions (VNFs) (deploy, start, stop or update); (ii) efficiency, resources can be adequately consumed due to the increased flexibility of the network infrastructure; and (iii) reduced costs, due to the ability of sharing hardware resources. To this end, multiple challenges must be addressed to effectively leverage of all these benefits. Network Function Virtualization envisioned the concept of virtual network, resulting in a key enabler of 5G networks flexibility, Network Slicing. This new paradigm represents a new way to operate mobile networks where the underlying infrastructure is "sliced" into logically separated networks that can be customized to the specific needs of the tenant. This approach also enables the ability of instantiate VNFs at different locations of the infrastructure, choosing their optimal placement based on parameters such as the requirements of the service traversing the slice or the available resources. This decision process is called orchestration and involves all the VNFs withing the same network slice. The orchestrator is the entity in charge of managing network slices. Hands-on experiments on network slicing are essential to understand its benefits and limits, and to validate the design and deployment choices. While some network slicing prototypes have been built for Radio Access Networks (RANs), leveraging on the wide availability of radio hardware and open-source software, there is no currently open-source suite for end-to-end network slicing available to the research community. Similarly, orchestration mechanisms must be evaluated as well to properly validate theoretical solutions addressing diverse aspects such as resource assignment or service composition. This thesis contributes on the study of the mobile networks evolution regarding its softwarization and cloudification. We identify software patterns for network function virtualization, including the definition of a novel mobile architecture that squeezes the virtualization architecture by splitting functionality in atomic functions. Then, we effectively design, implement and evaluate of an open-source network slicing implementation. Our results show a per-slice customization without paying the price in terms of performance, also providing a slicing implementation to the research community. Moreover, we propose a framework to flexibly re-orchestrate a virtualized network, allowing on-the-fly re-orchestration without disrupting ongoing services. This framework can greatly improve performance under changing conditions. We evaluate the resulting performance in a realistic network slicing setup, showing the feasibility and advantages of flexible re-orchestration. Lastly and following the required re-design of network functions envisioned during the study of the evolution of mobile networks, we present a novel pipeline architecture specifically engineered for 4G/5G Physical Layers virtualized over clouds. The proposed design follows two objectives, resiliency upon unpredictable computing and parallelization to increase efficiency in multi-core clouds. To this end, we employ techniques such as tight deadline control, jitter-absorbing buffers, predictive Hybrid Automatic Repeat Request, and congestion control. Our experimental results show that our cloud-native approach attains > 95% of the theoretical spectrum efficiency in hostile environments where stateof- the-art architectures collapse.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Francisco Valera Pintor.- Secretario: Vincenzo Sciancalepore.- Vocal: Xenofon Fouka

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

Landscape of IoT security

The last two decades have experienced a steady rise in the production and deployment of sensing-and-connectivity-enabled electronic devices, replacing “regular” physical objects. The resulting Internet-of-Things (IoT) will soon become indispensable for many application domains. Smart objects are continuously being integrated within factories, cities, buildings, health institutions, and private homes. Approximately 30 years after the birth of IoT, society is confronted with significant challenges regarding IoT security. Due to the interconnectivity and ubiquitous use of IoT devices, cyberattacks have widespread impacts on multiple stakeholders. Past events show that the IoT domain holds various vulnerabilities, exploited to generate physical, economic, and health damage. Despite many of these threats, manufacturers struggle to secure IoT devices properly. Thus, this work overviews the IoT security landscape with the intention to emphasize the demand for secured IoT-related products and applications. Therefore, (a) a list of key challenges of securing IoT devices is determined by examining their particular characteristics, (b) major security objectives for secured IoT systems are defined, (c) a threat taxonomy is introduced, which outlines potential security gaps prevalent in current IoT systems, and (d) key countermeasures against the aforementioned threats are summarized for selected IoT security-related technologies available on the market

Edge caching with mobility prediction in virtualized LTE mobile networks

Abstract Mobile Edge Computing enables the deployment of services, applications, content storage and processing in close proximity to mobile end users. This highly distributed computing environment can be used to provide ultra-low latency, precise positional awareness and agile applications, which could significantly improve user experience. In order to achieve this, it is necessary to consider next-generation paradigms such as Information-Centric Networking and Cloud Computing, integrated with the upcoming 5th Generation networking access. A cohesive end-to-end architecture is proposed, fully exploiting Information-Centric Networking together with the Mobile Follow-Me Cloud approach, for enhancing the migration of content-caches located at the edge of cloudified mobile networks. The chosen content-relocation algorithm attains content-availability improvements of up to 500 when a mobile user performs a request and compared against other existing solutions. The performed evaluation considers a realistic core-network, with functional and non-functional measurements, including the deployment of the entire system, computation and allocation/migration of resources. The achieved results reveal that the proposed architecture is beneficial not only from the users’ perspective but also from the providers point-of-view, which may be able to optimize their resources and reach significant bandwidth savings

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system

Resource management with adaptive capacity in C-RAN

This work was supported in part by the Spanish ministry of science through the projectRTI2018-099880-B-C32, with ERFD funds, and the Grant FPI-UPC provided by theUPC. It has been done under COST CA15104 IRACON EU project.Efficient computational resource management in 5G Cloud Radio Access Network (CRAN) environments is a challenging problem because it has to account simultaneously for throughput, latency, power efficiency, and optimization tradeoffs. This work proposes the use of a modified and improved version of the realistic Vienna Scenario that was defined in COST action IC1004, to test two different scale C-RAN deployments. First, a large-scale analysis with 628 Macro-cells (Mcells) and 221 Small-cells (Scells) is used to test different algorithms oriented to optimize the network deployment by minimizing delays, balancing the load among the Base Band Unit (BBU) pools, or clustering the Remote Radio Heads (RRH) efficiently to maximize the multiplexing gain. After planning, real-time resource allocation strategies with Quality of Service (QoS) constraints should be optimized as well. To do so, a realistic small-scale scenario for the metropolitan area is defined by modeling the individual time-variant traffic patterns of 7000 users (UEs) connected to different services. The distribution of resources among UEs and BBUs is optimized by algorithms, based on a realistic calculation of the UEs Signal to Interference and Noise Ratios (SINRs), that account for the required computational capacity per cell, the QoS constraints and the service priorities. However, the assumption of a fixed computational capacity at the BBU pools may result in underutilized or oversubscribed resources, thus affecting the overall QoS. As resources are virtualized at the BBU pools, they could be dynamically instantiated according to the required computational capacity (RCC). For this reason, a new strategy for Dynamic Resource Management with Adaptive Computational capacity (DRM-AC) using machine learning (ML) techniques is proposed. Three ML algorithms have been tested to select the best predicting approach: support vector machine (SVM), time-delay neural network (TDNN), and long short-term memory (LSTM). DRM-AC reduces the average of unused resources by 96 %, but there is still QoS degradation when RCC is higher than the predicted computational capacity (PCC). For this reason, two new strategies are proposed and tested: DRM-AC with pre-filtering (DRM-AC-PF) and DRM-AC with error shifting (DRM-AC-ES), reducing the average of unsatisfied resources by 99.9 % and 98 % compared to the DRM-AC, respectively

View on 5G Architecture: Version 1.0

The current white paper focuses on the produced results after one year research mainly from 16 projects working on the abovementioned domains. During several months, representatives from these projects have worked together to identify the key findings of their projects and capture the commonalities and also the different approaches and trends. Also they have worked to determine the challenges that remain to be overcome so as to meet the 5G requirements. The goal of 5G Architecture Working Group is to use the results captured in this white paper to assist the participating projects achieve a common reference framework. The work of this working group will continue during the following year so as to capture the latest results to be produced by the projects and further elaborate this reference framework. The 5G networks will be built around people and things and will natively meet the requirements of three groups of use cases: • Massive broadband (xMBB) that delivers gigabytes of bandwidth on demand • Massive machine-type communication (mMTC) that connects billions of sensors and machines • Critical machine-type communication (uMTC) that allows immediate feedback with high reliability and enables for example remote control over robots and autonomous driving. The demand for mobile broadband will continue to increase in the next years, largely driven by the need to deliver ultra-high definition video. However, 5G networks will also be the platform enabling growth in many industries, ranging from the IT industry to the automotive, manufacturing industries entertainment, etc. 5G will enable new applications like for example autonomous driving, remote control of robots and tactile applications, but these also bring a lot of challenges to the network. Some of these are related to provide low latency in the order of few milliseconds and high reliability compared to fixed lines. But the biggest challenge for 5G networks will be that the services to cater for a diverse set of services and their requirements. To achieve this, the goal for 5G networks will be to improve the flexibility in the architecture. The white paper is organized as follows. In section 2 we discuss the key business and technical requirements that drive the evolution of 4G networks into the 5G. In section 3 we provide the key points of the overall 5G architecture where as in section 4 we elaborate on the functional architecture. Different issues related to the physical deployment in the access, metro and core networks of the 5G network are discussed in section 5 while in section 6 we present software network enablers that are expected to play a significant role in the future networks. Section 7 presents potential impacts on standardization and section 8 concludes the white paper

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