Many network applications, eg, industrial control, demand ultra-low latency (ULL) However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds The IEEE 8021 time sensitive networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF deterministic networking (DetNet) standards seek to provide the complementary network layer ULL support This paper provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management Throughout, we identify the pitfalls and limitations of the existing standards and research studies This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Software defined networking (SDN) decouples the control plane from the data plane of forwarding devices. This separation provides several benefits, including the simplification of network management and control. However, due to a variety of reasons, such as budget constraints and fear of downtime, many organizations are reluctant to fully deploy SDN. Partially deploying SDN through the placement of a limited number of SDN devices among legacy (traditional) network devices, forms a so-called hybrid SDN network. While hybrid SDN networks provide many of the benefits of SDN and have a wide range of applications, they also pose several challenges. These challenges have recently been addressed in a growing body of literature on hybrid SDN network structures and protocols. This paper presents a comprehensive up-to-date survey of the research and development in the field of hybrid SDN networks. We have organized the survey into five main categories, namely hybrid SDN network deployment strategies, controllers for hybrid SDN networks, protocols for hybrid SDN network management, traffic engineering mechanisms for hybrid SDN networks, as well as testing, verification, and security mechanisms for hybrid SDN networks. We thoroughly survey the existing hybrid SDN network studies according to this taxonomy and identify gaps and limitations in the existing body of research. Based on the outcomes of the existing research studies as well as the identified gaps and limitations, we derive guidelines for future research on hybrid SDN networks.

Hybrid SDN Networks: A Survey of Existing Approaches

Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute- and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions.

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Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Communication networks are the key enabling technology for our digital society. In order to sustain their critical services in the future, communication networks need to flexibly accommodate new requirements and changing contexts due to emerging diverse applications. In contrast to traditional networking technologies, software-oriented networking concepts, such as software-defined networking (SDN) and network function virtualization (NFV), provide ample opportunities for highly flexible network operations, enabling fast and simple adaptation of network resources and flows. This paper identifies the opportunities and challenges of adaptable softwarized networks and introduces a conceptual framework for adaptations in softwarized networks. We first explain how softwarized networks contribute to network adaptability through the functional primitives observation, composition, and control. We review the wide range of options for fine-granular observations as well as fine-granular composition and control provided by SDN and NFV. The multitude of fine-granular “tuning knobs” in adaptable softwarized networks complicates the decision making, which is the main focus of this paper. We propose to enhance the functional primitives observation, composition, and control with data-driven decision making, e.g., machine learning modules, resulting in deep observation, composition, and control. The data-driven decision making modules can learn and react to changes in the environment, e.g., new flow demands, so as to support meaningful decision making for adaptation in softwarized networks. Finally, we make the case for employing the concept of empowerment to realize truly “self-driving” networks.

/pdf/adaptable-and-data-driven-softwarized-networks-review-1c2wx52lj2.pdf

Adaptable and Data-Driven Softwarized Networks: Review, Opportunities, and Challenges

Existing radio access networks (RANs) allow only for very limited sharing of the communication and computation resources among wireless operators and heterogeneous wireless technologies. We introduce the LayBack architecture to facilitate communication and computation resource sharing among different wireless operators and technologies. LayBack organizes the RAN communication and multi-access edge computing (MEC) resources into layers, including a devices layer, a radio node (enhanced Node B and access point) layer, and a gateway layer. LayBack positions the coordination point between the different operators and technologies just behind the gateways and thus consistently decouples the fronthaul from the backhaul. The coordination point is implemented through a software defined networking (SDN) switching layer that connects the gateways to the backhaul (core) network layer. A unifying SDN orchestrator implements an SDN-based management framework that centrally manages the fronthaul and backhaul communication and computation resources and coordinates the cooperation between different wireless operators and technologies. We illustrate the capabilities of the introduced LayBack architecture and SDN-based management framework through a case study on a novel fluid cloud RAN (CRAN) function split. The fluid CRAN function split partitions the RAN functions into function blocks that are flexibly assigned to MEC nodes, effectively implementing the RAN functions through network function virtualization. We find that for non-uniform call arrivals, the computation of the function blocks with resource sharing among operators increases a revenue rate measure by more than 25% compared to the conventional CRAN where each operator utilizes only its own resources.

LayBack: SDN Management of Multi-Access Edge Computing (MEC) for Network Access Services and Radio Resource Sharing

We study uncoordinated random access over fading channels where each user independently decides whether to send a packet or not to a common receiver at any given time slot. Specifically, we develop an information theoretic formulation to characterize the overall system throughput. We consider two scenarios: classical slotted ALOHA, where no multiuser detection (MUD) capability is available and slotted ALOHA with MUD. In each case, in order to maximize the system throughput, we provide methods to obtain the optimal rates and channel activity probabilities using the user distances to the receiver (or, equivalently, their average signal to noise ratios) assuming a Rayleigh block fading channel. The results demonstrate that the newly proposed optimal rate selection solutions offer significant increase in the expected system throughputs compared to the “same rate to all users” approach commonly used in the literature. In addition to the overall throughput optimization, we also address the issue of fairness among users and propose approaches guaranteeing a minimum amount of individual throughput to each user, and design systems with limited individual outage probabilities for increased energy efficiency and reduced delay.

/pdf/rate-selection-for-wireless-random-access-networks-over-1ude8fbrqd.pdf

Rate Selection for Wireless Random Access Networks Over Block Fading Channels

With the emergence of small cell networks and fifth-generation (5G) wireless networks, the backhaul becomes increasingly complex. This study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to address this backhaul resource allocation problem, we introduce a novel backhaul optimization methodology in the context of the recently proposed LayBack SDN backhaul architecture. In particular, we explore the decomposition of the central optimization problem into a layered dual decomposition model that matches the architectural layers of the LayBack backhaul architecture. In order to promote scalability and responsiveness, we employ different timescales, i.e., fast timescales at the radio nodes and slower timescales in the higher LayBack layers that are closer to the central SDN orchestrator. We numerically evaluate the scalable layered optimization for a specific case of the LayBack backhaul architecture with four layers, namely a radio node (eNB) layer, a gateway layer, an operator layer, and central coordination in an SDN orchestrator layer. The coordinated sharing of the total backhaul capacity among multiple operators lowers the queue lengths compared to the conventional backhaul without sharing among operators.

https://www.mdpi.com/2079-9292/8/9/937/pdf

A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing

We describe a distributed framework for resource sharing problems that arise in communications, micro-economics, and various networking applications. In particular, we consider a hierarchical multi-layer decomposition for network utility maximization (ML-NUM), where functionalities are assigned to different layers. The proposed methodology creates solutions with central management and distributed computations to the resource allocation problems. In non-stationary environments, the technique aims to respond quickly to the dynamics of the network by decreasing delay by partially shifting the communication and computational burden to the network edges. Our main contribution is a detailed analysis under the assumption that the network changes are on the same time-scale as the convergence time of the algorithms used for local computations. Moreover, assuming strong concavity and smoothness of the users’ objective functions, and under some stability conditions for each layer, we present convergence rates and optimality bounds for the ML-NUM framework. In addition, the main benefits of the proposed method are demonstrated with numerical examples.

Multi-Layer Decomposition of Network Utility Maximization Problems

This paper describes a unifying optimization framework to share backhaul network resources across different operators and wireless platforms. The architecture we consider, named LayBack, requires introducing a unifying Software Defined Network (SDN) orchestrator, sited where their respective traffic streams meet: at the wireless network backhaul. The work we present proposes a scalable decomposition of the resource allocation problem across different layers and time-scales.

Nurullah Karakoc

Papers

LayBack: SDN Management of Multi-Access Edge Computing (MEC) for Network Access Services and Radio Resource Sharing

Rate Selection for Wireless Random Access Networks Over Block Fading Channels

A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing

Multi-Layer Decomposition of Network Utility Maximization Problems

Layered Cooperative Resource Sharing at a Wireless SDN Backhaul