Enabling Scalable and Sustainable Softwarized 5G Environments

The fifth generation of telecommunication systems (5G) is foreseen to play a fundamental role in our socio-economic growth by supporting various and radically new vertical applications (such as Industry 4.0, eHealth, Smart Cities/Electrical Grids, to name a few), as a one-fits-all technology that is enabled by emerging softwarization solutions \u2013 specifically, the Fog, Multi-access Edge Computing (MEC), Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) paradigms. Notwithstanding the notable potential of the aforementioned technologies, a number of open issues still need to be addressed to ensure their complete rollout. This thesis is particularly developed towards addressing the scalability and sustainability issues in softwarized 5G environments through contributions in three research axes: a) Infrastructure Modeling and Analytics, b) Network Slicing and Mobility Management, and c) Network/Services Management and Control. The main contributions include a model-based analytics approach for real-time workload profiling and estimation of network key performance indicators (KPIs) in NFV infrastructures (NFVIs), as well as a SDN-based multi-clustering approach to scale geo-distributed virtual tenant networks (VTNs) and to support seamless user/service mobility; building on these, solutions to the problems of resource consolidation, service migration, and load balancing are also developed in the context of 5G. All in all, this generally entails the adoption of Stochastic Models, Mathematical Programming, Queueing Theory, Graph Theory and Team Theory principles, in the context of Green Networking, NFV and SDN

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Resource Orchestration in Softwarized Networks

Network softwarization is an emerging research area that is envisioned to revolutionize the way network infrastructure is designed, operated, and managed today. Contemporary telecommunication networks are going through a major transformation, and softwarization is recognized as a crucial enabler of this transformation by both academia and industry. Softwarization promises to overcome the current ossified state of Internet network architecture and evolve towards a more open, agile, flexible, and programmable networking paradigm that will reduce both capital and operational expenditures, cut-down time-to-market of new services, and create new revenue streams. Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are two complementary networking technologies that have established themselves as the cornerstones of network softwarization. SDN decouples the control and data planes to provide enhanced programmability and faster innovation of networking technologies. It facilitates simplified network control, scalability, availability, flexibility, security, cost-reduction, autonomic management, and fine-grained control of network traffic. NFV utilizes virtualization technology to reduce dependency on underlying hardware by moving packet processing activities from proprietary hardware middleboxes to virtualized entities that can run on commodity hardware. Together SDN and NFV simplify network infrastructure by utilizing standardized and commodity hardware for both compute and networking; bringing the benefits of agility, economies of scale, and flexibility of data centers to networks. Network softwarization provides the tools required to re-architect the current network infrastructure of the Internet. However, the effective application of these tools requires efficient utilization of networking resources in the softwarized environment. Innovative techniques and mechanisms are required for all aspects of network management and control. The overarching goal of this thesis is to address several key resource orchestration challenges in softwarized networks. The resource allocation and orchestration techniques presented in this thesis utilize the functionality provided by softwarization to reduce operational cost, improve resource utilization, ensure scalability, dynamically scale resource pools according to demand, and optimize energy utilization

Future RAN architecture: SD-RAN through a general-purpose processing platform

In this article, we identify and study the potential of an integrated deployment solution for energy-efficient cellular networks combining the strengths of two very active current research themes: 1) software-defined radio access networks (SD-RANs) and 2) decoupled signaling and data transmissions, or beyond cellular green generation (BCG2) architecture, for enhanced energy efficiency. While SD-RAN envisions a decoupled centralized control plane and data-forwarding plane for flexible control, the BCG2 architecture calls for decoupling coverage from the capacity and coverage provided through an always-on low-power signaling node for a larger geographical area; the capacity is catered by various on-demand data nodes for maximum energy efficiency. In this article, we show that a combined approach that brings both specifications together can not only achieve greater benefits but also facilitate faster realization of both technologies. We propose the idea and design of a signaling controller that acts as a signaling node to provide always-on coverage, consuming low power, and at the same time host the control plane functions for the SDRAN through a general-purpose processing platform. The phantom cell concept is also a similar idea where a normal macrocell provides interference control to densely deployed small cells, although our initial results show that the integrated architecture has a much greater potential for energy savings than phantom cells

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

QUALITY-OF-SERVICE PROVISIONING FOR SMART CITY APPLICATIONS USING SOFTWARE-DEFINED NETWORKING

In the current world, most cities have WiFi Access Points (AP) in every nook and corner. Hence upraising these cities to the status of a smart city is a more easily achievable task than before. Internet-of-Things (IoT) connections primarily use WiFi standards to form the veins of a smart city. Unfortunately, this vast potential of WiFi technology in the genesis of smart cities is somehow compromised due to its failure in meeting unique Quality-of-Service (QoS) demands of smart city applications. Out of the following QoS factors; transmission link bandwidth, packet transmission delay, jitter, and packet loss rate, not all applications call for the all of the factors at the same time. Since smart city is a pool of drastically unrelated services, this variable demand can actually be advantageous to optimize the network performance. This thesis work is an attempt to achieve one of those QoS demands, namely packet delivery latency. Three algorithms are developed to alleviate traffic load imbalance at APs so as to reduce packet forwarding delay. Software-Defined Networking (SDN) is making its way in the network world to be of great use and practicality. The algorithms make use of SDN features to control the connections to APs in order to achieve the delay requirements of smart city services. Real hardware devices are used to imitate a real-life scenario of citywide coverage consisting of WiFi devices and APs that are currently available in the market with neither of those having any additional requirements such as support for specific roaming protocol, running a software agent or sending probe packets. Extensive hardware experimentation proves the efficacy of the proposed algorithms