DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls

Advances in network programmability enable operators to 'slice' the physical infrastructure into independent logical networks. By this approach, each network slice aims to accommodate the demands of increasingly diverse services. However, precise allocation of resources to slices across future 5G millimetre-wave backhaul networks, to optimise the total network utility, is challenging. This is because the performance of different services often depends on conflicting requirements, including bandwidth, sensitivity to delay, or the monetary value of the traffic incurred. In this paper, we put forward a general rate utility framework for slicing mm-wave backhaul links, encompassing all known types of service utilities, i.e. logarithmic, sigmoid, polynomial, and linear. We then introduce DELMU, a deep learning solution that tackles the complexity of optimising non-convex objective functions built upon arbitrary combinations of such utilities. Specifically, by employing a stack of convolutional blocks, DELMU can learn correlations between traffic demands and achievable optimal rate assignments. We further regulate the inferences made by the neural network through a simple 'sanity check' routine, which guarantees both flow rate admissibility within the network's capacity region and minimum service levels. The proposed method can be trained within minutes, following which it computes rate allocations that match those obtained with state-of-the-art global optimisation algorithms, yet orders of magnitude faster. This confirms the applicability of DELMU to highly dynamic traffic regimes and we demonstrate up to 62% network utility gains over a baseline greedy approach.Comment: remove LaTeX remains in abstract; change the font for acrony

Efficient radio resource allocation in SDN/NFV based mobile cellular networks under the complete sharing policy

Novel networking paradigms, such as software-defined networking (SDN) and network function virtualisation (NFV), introduce new opportunities in the design of next-generation mobile networks. The present work investigates the benefits of the emerging SDN and NFV technologies on the radio resource management (RRM) in mobile cellular networks. In particular, the aim of the proposed RRM scheme is to enable an efficient and flexible radio resource allocation in order to assure quality of experience of mobile users. The authors consider the orthogonal frequency division multiple access scheme and the complete radio resource sharing policy. To enable time- and space-efficient resource allocation, the authors investigate the applicability of the well-known Kaufman–Roberts recursion in the context of new architectural and functional changes of SDN/NFV based mobile environments. Finally, they discuss the applicability of the proposed approach for more complicated resource sharing policies

A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Networks

The 5G networks have the capability to provide high compatibility for the new applications, industries, and business models. These networks can tremendously improve the quality of life by enabling various use cases that require high data-rate, low latency, and continuous connectivity for applications pertaining to eHealth, automatic vehicles, smart cities, smart grid, and the Internet of Things (IoT). However, these applications need secure servicing as well as resource policing for effective network formations. There have been a lot of studies, which emphasized the security aspects of 5G networks while focusing only on the adaptability features of these networks. However, there is a gap in the literature which particularly needs to follow recent computing paradigms as alternative mechanisms for the enhancement of security. To cover this, a detailed description of the security for the 5G networks is presented in this article along with the discussions on the evolution of osmotic and catalytic computing-based security modules. The taxonomy on the basis of security requirements is presented, which also includes the comparison of the existing state-of-the-art solutions. This article also provides a security model, "CATMOSIS", which idealizes the incorporation of security features on the basis of catalytic and osmotic computing in the 5G networks. Finally, various security challenges and open issues are discussed to emphasize the works to follow in this direction of research.Comment: 34 pages, 7 tables, 7 figures, Published In 5G Enabled Secure Wireless Networks, pp. 69-102. Springer, Cham, 201

The road beyond 5G:a vision and insight of the key technologies

Abstract As 5G enters a stable phase in terms of system architecture, 3GPP Release 17 starts to investigate advanced features that would shape the evolution toward 6G. This paper provides an insightful analysis for mobile networks Beyond 5G (B5G) considering the advancements and implications introduced by the evolution of softwarization, agile control and deterministic services. It elaborates the 5G landscape, also investigating new business prospects and the emerging use cases, which will open new horizons for accelerating the market penetration of vertical services. It then overviews the key technologies that constitute the pillars for the evolution beyond 5G considering new radio paradigms, micro-service oriented core network, native IP based user plane, network analytics and the support of the low latency- high reliability transport layer. The open challenges considering both technical and business aspects are then overviewed, elaborating the footprint of softwarization, security and trust as well as distributed architectures and services toward 6G

QoE-SD N APP: A Rate-guided QoE-aware SDN-APP for HTTP Adaptive Video Streaming

While video streaming has dominated the Internet traffic, video service providers (VSPs) compete on how to assure the best quality of experience (QoE) to their customers. HTTP Adaptive Streaming (HAS) has become the de facto way that helps VSPs work-around potential network bottlenecks that inevitably cause stallings. However, HAS-alone cannot guarantee a seamless viewing experience, since this highly relies on the mobile network operators' (MNOs) infrastructure and evolving network conditions. Software-defined networking (SDN) has brought new perspectives to this traditional paradigm where VSPs and MNOs are isolated, allowing the latter to open their network for more flexible, service-oriented programmability. This paper takes advantage of recent standardization trends in SDN and proposes a programmable QoE-SDN App, enabling network exposure feedback from MNOs to VSPs towards network-aware video segment selection and caching, in the context of HAS. The video selection problem is formulated using Knapsack optimization and relaxed to partial sub-problems that provide segment encodings that can mitigate stallings. Furthermore, a mobility prediction mechanism based on the Self-similar Least-Action Walk model is introduced, toward proactive segment caching. A number of use cases, enabled by the QoE-SDN App, are designed to evaluate the proposed scheme, revealing QoE benefits for VSPs and bandwidth savings for MNOs. © 2018 IEEE