The emerging intelligent reflecting surface (IRS) technology introduces the potential of controlled light propagation in visible light communication (VLC) systems. This concept opens the door for new applications in which the channel itself can be altered to achieve specific key performance indicators. In this paper, for the first time in the open literature, we investigate the role that IRSs can play in enhancing the link reliability in VLC systems employing non-orthogonal multiple access (NOMA). We propose a framework for the joint optimisation of the NOMA and IRS parameters and show that it provides significant enhancements in link reliability. For example, the bit-error-rate of the NOMA user in the first decoding order is reduced to the order of 10−6using the proposed framework compared to an error floor of 10−2under fixed power allocation and fixed IRS reflection coefficients. The enhancement is even more pronounced when the VLC channel is subject to blockage and random device orientation.

5G is the fifth generation of cellular networks. It enables billions of connected devices to gather and share information in real time; a key facilitator in Industrial Internet of Things (IoT) applications. It has more capabilities in terms of bandwidth, latency/delay, processing powers and flexibility to utilize either edge or cloud resources. Furthermore, 6G is expected to be equipped with the new capability to converge ubiquitous communication, computation, sensing and controlling for a variety of sectors, which heightens the complexity in a more heterogeneous environment This increased complexity, combined with energy efficiency and Service Level Agreement (SLA) requirements makes application of Machine Learning (ML) and distributed ML necessary. A decentralized approach stemming from distributed learning is a very attractive option compared with a centralized architecture for model learning and inference. Distributed ML exploits recent Artificial Intelligence (AI) technology advancements to allow collaborated ML, whilst safeguarding private data, minimizing both communication and computation overhead along with addressing ultra-low latency requirements. In this paper, we review a number of distributed ML architectures and designs, that focus on optimizing communication, computation and resource distribution. Privacy, information security and compute frameworks, are also analyzed and compared with respect to different distributed ML approaches. We summarize the major contributions and trends in this area and highlight the potential of distributed ML to help researchers and practitioners make informed decisions on selecting the right ML approach for 5G and Beyond related AI applications. To enable distributed ML for 5G and Beyond, communication, security, and computing platform often counter balance each other, thus, consideration and optimization of these aspects at an overall system level is crucial to realize the full potential of AI for 5G and Beyond. These different aspects do not only pertain to 5G, but will also enable careful design of distributed machine learning architectures to circumvent the same hurdles that will inevitably burden 5G and Beyond network generations. This is the first survey paper that brings together all these aspects for distributed ML.

Digital twin (DT) technology has recently gathered pace in the engineering communities as it allows for the convergence of the real structure and its digital counterpart throughout their entire life-cycle. With the rapid development of supporting technologies, including machine learning (ML), 5G/6G, cloud computing, and Internet of Things, DT has been moving progressively from concept to practice. In this article, a DT framework based on cloud computing and deep learning (DL) for structural health monitoring is proposed to efficiently perform real-time monitoring and proactive maintenance. The framework consists of structural components, device measurements, and digital models formed by combining different submodels, including mathematical, finite element, and ML ones. The data interaction among physical structure, digital model, and human interventions are enhanced by using cloud computing infrastructure and a user-friendly web application. The feasibility of the proposed framework is demonstrated via case studies of damage detection of model bridge and real bridge structures using DL algorithms, with high accuracy of 92%.

Network slicing is a key enabler for next-generation 5G services. Slices are designed to offer different services by conjoining virtual network functions (VNFs) through a logical network. We propose a dynamic slicing algorithm, which is based on utility theory and facilitates the growth, provisioning, dimensioning and deletion of network slices. The proposed algorithm is based on a utility model that regulates the virtual topology of VNFs in data-centers. The algorithm optimizes the number of slices as well as instantiated VNFs, thereby creating/modifying and destroying network slices of appropriate granularity. In addition, we also propose a stochastic optimization formulation, which handles uncertainty in service requests. We simulate the algorithm for a multi-data-center model with a large set of slices. Our results indicate dynamism, robustness and scalability of the proposed algorithm.

Mobile data offloading on smaller cells such as Wi-Fi comes as a natural solution to boost cellular networks capacity and keep up with the rapid increase of mobile data traffic demand. In this paper, we propose an offloading mechanism through the abstraction of Software-defined Networking (SDN) in the mobile backhaul to provide programmable offloading policy derivation that are aware of users and applications as well as the condition of wireless network. The proposed mechanism considers the real-time network condition to derive the offloading policies and efficiently accommodate the traffic in both LTE and Wi-Fi networks. Numerical results prove that the proposed approach can significantly improves dropping rate of the incoming traffic with using more real-time and dynamic decisions for offloading.

Network operators are faced with the challenge of deploying and managing middleboxes (also called inline services) such as firewalls within their broadband access, datacenter or enterprise networks. Due to the lack of available protocols to route traffic through middleboxes, operators still rely on error-prone and complex low-level configurations to coerce traffic through the desired set of middleboxes. Built upon the recent software-defined networking (SDN) architecture and OpenFlow protocol, this paper proposes StEERING, short for SDN inlinE sERvices and forwardlNG. It is a scalable framework for dynamically routing traffic through any sequence of middleboxes. With simple centralized configuration, StEERING can explicitly steer different types of flows through the desired set of middleboxes, scaling at the level of per-subscriber and per-application policies. With its capability to support flexible routing, we further propose an algorithm to select the best locations for placing services, such that the performance is optimized. Overall, StEERING allows network operators to monetize their middlebox deployment in new ways by allowing subscribers flexibly to select available network services.

This paper discusses the benefits of applying software defined networking (SDN) to circuit based transport networks. It first establishes the need for SDN in the context of transport networks. This paper argues that the use of SDN in the transport layers could be the enabler for both packet-optical integration and improved transport network applications. Then, this paper proposes extensions to OpenFlow 1.1 to achieve control of switches in multi-technology transport layers. The approach presented in this paper is simple, yet it distinguishes itself from similar work by its friendliness with respect to the current transport layer control plane based on generalized multiprotocol label switching (GMPLS). This is important as it will enable an easier and gradual injection of SDN into existing transport networks. This paper is completed with a few use case applications of SDN in transport networks.

For new social service providers (freshmen), it is critical to determine where to deploy the computational resources to best accommodate future client requests. Existing proposals on server placement rely on collecting and analyzing request history from servers that are already running, which are not so useful to those starting new online social network services (OSNs). In this work, we aim at helping new OSN providers with intelligent server placement by exploring available public information from existing social network communities. We explore the commonality between the selected set of server locations from multiple OSNs and utilize such similarity for future OSNs to select their server locations. The similarity is ultimately due to the fact that the underlying human relationship is relatively consistent across OSNs.

Applications over cellular networks now range from operator–consumer applications (e.g., mobile television, voice-over-ip, video conferencing), peer-to-peer applications (e.g., instant messaging), machine-to-machine applications (e.g., data telemetry and automotive applications), mobile web services (e.g., music and video streaming), and social networking applications. The current approach for developing mobile applications appears to focus on utilizing template-based application-development kits provided by platform developers (e.g., Google's Android, Apple's iOS, or Nokia's Symbian) to capture application designs and install them on the runtime platforms through use of code generators tied to particular versions of the platform. It is still unclear as to how an application developer (or network operator) conceptualizes the features of a mobile application in a platform-independent way, identifies its utility and explores its impact on the user, or further refines the choice of technology, platform, and mobility/interactivity requirements. This paper attempts to offer some guidelines, based on recent research in the industry and academia in these areas, toward the design and development of successful mobile applications that can utilize the capabilities of the next generation of cellular networks. We provide an overview of the growing trends of the rich multimedia and real-time mobile applications, including the diversity of application types, their impact on the enterprise and consumer, their traffic volumes, and their load and communication patterns. In addition to the overall trend analysis, we also study the design choices that are to be made, and how they are realized, and also describe how the platforms (client and server) may be implemented. Additionally, we focus on mobile video applications according to their communication characteristics and their distinct demands on the cellular network. We also present an analysis of device and network application programming interfaces (API) that form the basic building blocks for efficient and secure mobile application development of the future.

In today's networks, a node usually has a static role that cannot be easily changed without an expensive upgrade. In MPLS architecture, despite the flexible forwarding data plane not tied to a single forwarding technology, each MPLS node has to be dedicated for a specific role depending on its position in the edge or the core of the MPLS network domain. This paper proposes an approach to address the flexibility of an MPLS node to play multiple roles for different MPLS domains built on top of an underlying OpenFlow-enabled physical network. The pipelined approach through tables introduced in the version 1.1 and later of the OpenFlow specification allows the change of the packet processing behavior by just updating the memory structures - such as the TCAM and hash tables. It exploits the power of the OpenFlow rules-based paradigm to demonstrate the high level programmability to achieve the deossification of an MPLS infrastructure. In order to validate our proposal, we have implemented our approach over a 100Gbps switch box built on network processors and tested it with three applications to evaluate its flexibility. The results show that the local software to hardware update of a Label Switched Path (LSP) can be made in 2.2ms, in average, and the deployment of a Label Switched Router (LSR) application with 400 labels takes only 392.5ms.

The split architecture network assumes a logically centralized controller, which is physically separated from a large set of data plane forwarding switches. When the control plane becomes decoupled from the data plane, the requirement to the failure resilience and recovery mechanisms changes. In this work we investigate one of the most important practical issues in split architecture deployment, the placement of controllers in a given network. We first demonstrate that the location of controllers have high impact on the network resilience using a real network topology. Motivated by such observation, we propose a min-cut based controller placement algorithm and compare it with greedy based approach. Our simulation results show significant reliability improvements with an intelligent placement strategy. Our work is the first attempt on the resilience properties of a split architecture network.

The Border Gateway Protocol (BGP) is the de facto interdomain routing protocol on the Internet which controls the packet forwarding behavior on the data plane. It has significant impact on the well-being of the global Internet. Over the past ten years, there has been a large body of studies conducted on evaluating and improving the BGP performance. These studies develop tools using BGP data for identifying the Internet topology, AS relationships, and AS-level paths. More importantly BGP is the main data source for evaluating the Inter-domain routing performance and discovering routing anomalies such as prefix hijacking attacks. However, most of these studies focus on one or a few aspects of BGP in a short time period. Till today, the route monitoring system has been deployed for ten years and there has been a significant amount of criticisms on the bad performance of BGP. Our work is the first to critically examine and summarize BGP performance and its changes through time. We evaluate BGP from a diverse set of aspects ranging from routing diversity to convergence performance. We design a set of systematic statistical analysis to cope with the noise in data collection process. Due to the huge volume of data required for the analysis, we implement our evaluation system on top of the cloud computing platform from Amazon EC2. Our results provide a few insights on how to improve BGP and the Internet routing system in the future.

The AS path prepending approach in BGP is commonly used to perform inter-domain traffic engineering, such as inbound traffic load-balancing for multi-homed ASes. It artificially increases the length of the AS level path in BGP announcements by inserting its local AS number multiple times into outgoing EBGP announcement messages. In this work, we first present a comprehensive study on the characterization of Internet routing AS path prepending. We further propose an algorithm for computing the optimal padding strategies given multiple neighboring links. Our method considers the impact of AS relationship based local policies on ASPP's effectiveness. The algorithm can be used for three objectives, i.e., traffic load balancing, backup route provisioning, and bypassing a specific AS for security purposes, e.g., avoiding information censorship. We demonstrate the accuracy and effectiveness of our approach using real BGP data and traffic data from Abilene networks.