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Network slicing is a fundamental feature of 5G systems that allows the partitioning of a single network into a number of segregated logical networks, each optimized for a particular type of service, or dedicated to a particular cu...
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Network slicing is a fundamental feature of 5G systems that allows the partitioning of a single network into a number of segregated logical networks, each optimized for a particular type of service, or dedicated to a particular customer or application. While support for network slicing (e.g. identifiers, functions, signalling) is already defined in the latest 3GPP Release 15 specifications, solutions for efficient automated management of network slicing (e.g. automatic provisioning of slices) are still at a much more incipient stage, especially for what concerns the next-generation Radio Access Network (NG-RAN). In this context, and consistently with the new service-based management architecture defined by 3GPP for 5G systems, this paper presents a functional framework for the management of network slicing in a NG-RAN infrastructure, delineating the interfaces and information models necessary to support the dynamic and automatic deployment of RAN slices. A discussion on the complexity of such automation follows together with an illustrative description of the applicability of the overall framework and information models in the context of a neutral host provider scenario that offers RAN slices to third party service providers.
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Today, the Network Slicing technology is massively addressed by the research community. However, Network Slice (NS) modelling details from Standards Developing Organizations (SDOs) are not yet well considered for End-to-End (E2E) ...
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Today, the Network Slicing technology is massively addressed by the research community. However, Network Slice (NS) modelling details from Standards Developing Organizations (SDOs) are not yet well considered for End-to-End (E2E) NS implementations. In addition, each SDO develops standards targeting only a specific part of the NS architecture. Therefore, based on a profound analysis of the major existing works, this article explains first (i) a general architecture that clarifies the basic E2E network slicing functionality before diving deep into domain-specific visions. Then, (ii) it focuses at providing a survey stitching together the NS modelling works in Radio Access Networks (RAN), Core Networks (CN) and also Transport Networks (TN). The end goal is to clarify the E2E Network Slicing process from the service order request to the NS deployment and life-cycle management. Last, as there is no consensus on a specific information model in the Transport network domains (iii) we provide our vision on how several data models, developed by IETF working groups, can be integrated together in the context of the ACTN architecture in order to provision and manage Transport NSs.
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Commonly, the network configuration leans upon the operators' experience to operate network, including command-line configuration, middle-ware scripts, and troubleshooting. However, with the rise of neoteric B5G services, the manu...
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Commonly, the network configuration leans upon the operators' experience to operate network, including command-line configuration, middle-ware scripts, and troubleshooting. However, with the rise of neoteric B5G services, the manual way lacks flexibility and timeliness, resulting in an unsatisfactory level of configuration. It is necessary to consider a manual free configuration way for transport network. To cope with this problem, we present an intent-driven network architecture with self-adapting slicing policy and slices reconfiguration in an intent-orient manner. Aiming at intent request, intent analysis based on latent dirichlet allocation is introduced to establish the semantic graph to comprehend and enact the required slicing configuration language, namely intent translation. Then, in line with intent translation, we propose a self-adapted slicing policy generation and optimization base on deep reinforcement learning (SPG-RL) to find combined strategies that meet the intent requirements by dynamically integrating fine-grained slicing policies. Finally, deep neural evolution network (DNEN)-assisted model (SPG-RL-DNEN) is introduced to locate the incompatible slices at the millisecond level for slicing reconfiguration. When the network entropy reaches the threshold, SPG-RL-DNEN would reconfigure the incompatible slices for intent guarantee. The efficiency of our proposal are verified on enhanced SDN testbed.
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Leveraging on Network Function Virtualization (NFV) and Software Defined Networking (SDN), network slicing (NS) is recognized as a key technology that enables the 5G Infrastructure Provider (InP) to support diversified vertical se...
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Leveraging on Network Function Virtualization (NFV) and Software Defined Networking (SDN), network slicing (NS) is recognized as a key technology that enables the 5G Infrastructure Provider (InP) to support diversified vertical services over a shared common physical infrastructure. 5G end-to-end (E2E) NS is a logical virtual network that spans across the 5G network. Existing works on improving the reliability of the 5G mainly focus on reliable wireless communications, on the other hand, the reliability of an NS also refers to the ability of the NS system to provide continued service. Hence, in this work, we focus on enhancing the reliability of the NS to cope with physical network node failures, and we investigate the NS deployment problem to improve the reliability of the system represented by the NS. The reliability of an NS is enhanced by two means: firstly, by considering the topology information of an NS, critical virtual nodes are backed up to allow failure recovery; secondly, the embedding of the augmented NS virtual network is optimized for failure avoidance. We formulate the embedding of the augmented virtual network (AVN) to maximize the survivability of the NS system as the survivable AVN embedding (S-AVNE) problem through an Integer Linear Program (ILP) formulation. Due to the complexity of the problem, a heuristic algorithm is introduced. Finally, we conduct intensive simulations to evaluate the performance of our algorithm with regard to improving the reliability of the NS system.
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Network slicing has been proposed as a promising technology to fulfill users' specific service demands in 5G network. The important issues in this network paradigm are slices' reliability and online network slice embedding (NSE). ...
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Network slicing has been proposed as a promising technology to fulfill users' specific service demands in 5G network. The important issues in this network paradigm are slices' reliability and online network slice embedding (NSE). In this paper, an NSE model under the reliability constraints is established based on cloud radio access network architecture. The model jointly maximizes the number of network slice requests and minimizes the failure rate of NS. We further investigate the tradeoff between network stability and NS reliability. The NSE problem is formulated as a mixed integer linear program (MILP) and the heuristic algorithm named the queue-aware reliable embedding algorithm is proposed to improve the reliability of NSE. The Lyapunov optimization model is introduced to optimize the resource allocation while ensuring the stability of the queue. Furthermore, an NSE-online mechanism based on time window is proposed that can realize the online processing of the incoming NSRs. Both theoretical and simulation analyses are conducted and the results demonstrate that the proposed algorithm can improve the throughput while guaranteeing the network reliability and stability. (C) 2018 Published by Elsevier B.V.
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The Fifth Generation(5G) communication network is envisioned to provide heterogeneous services tailored to specific user demands. These services are diverse and can be generally categorized based on latency, bandwidth, reliability...
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The Fifth Generation(5G) communication network is envisioned to provide heterogeneous services tailored to specific user demands. These services are diverse and can be generally categorized based on latency, bandwidth, reliability, and connection density requirements. The 5G infrastructure providers are expected to employ network function virtualization, software-defined networking, and network slicing for cost-effective and efficient network resource allocation. In the 5G network, when an infrastructure provider receives a slice request, a slice admission control scheme is applied and an optimization algorithm is used to achieve predefined objectives. To this end, a number of slice admission control objectives, strategies and algorithms have been proposed. However, there is a need to present a coherent review and bridge the gap between many aspects of slice admission control. In this paper, we present the latest developments in this research area. Thus, we begin by introducing slice admission control and discuss background concepts associated with slicing. We then extend our discussion to slice admission objectives followed by the strategies and optimization algorithms. Finally, we conclude with a summary of analysis containing the optimization algorithms.
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The sixth-generation (6G) communication networks are envisioned to build a cloud-edge-terminal ecosystem that can provide various AI services for end devices. With the development of 6G networks, massive devices will consume incre...
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The sixth-generation (6G) communication networks are envisioned to build a cloud-edge-terminal ecosystem that can provide various AI services for end devices. With the development of 6G networks, massive devices will consume incredible computing and network resources for customized services. Mobile edge computing (MEC) has been regarded as a promising solution to relieve the pressure of the core network. The combination of unmanned aerial vehicles (UAVs) and MEC systems makes the edge server greatly improved in mobility and flexibility. However, it is difficult to manage and allocate appropriate resources for a large number of end devices. Meanwhile, the UAVs are limited in energy capacities and less stable than the fixed edge server. In this work, we propose a survivable resource slice embedding (SRSE) algorithm for UAV-assisted edge computing systems by leveraging network slicing technologies. A long short-term memory (LSTM) network is developed to obtain the future workloads of resource slices to reduce the slice embedding and re-embedding energy consumption simultaneously. By leveraging an open-source 5G trajectory dataset, the proposed SRSE algorithm is compared with two benchmark schemes. Finally, we provide extensive experimental results to illustrate that the SRSE algorithm improves the request acceptance ratio, slice recovery ratio, and reduces the slice recovery energy consumption significantly.
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The edge-cloud computing and network slicing have emerged as promising solutions to fulfill the diversity of IoT applications enabled by 5G and beyond. However, edge-cloud computing systems are composed of various hardware facilit...
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The edge-cloud computing and network slicing have emerged as promising solutions to fulfill the diversity of IoT applications enabled by 5G and beyond. However, edge-cloud computing systems are composed of various hardware facilities, leading to difficulties in hardware control and management. With network slicing, underlying resource sharing among multiple slice users is allowed, leading to potential attacks to the slice formulation processes and malicious usage of network slices that may result in inefficient resource utilization of the system. To address the aforementioned network slice security issue, we first propose a new systematic framework, named software-defined edge-cloud computing (SD-ECC), which applies standard software to control the hardware infrastructure regardless of vendor variations. With SD-ECC, resource slices are formulated by including storage and computational resources provided by edge and cloud servers. Then, we study an optimal slicing-based resource orchestration problem by considering slice-initiated attacks as possible adversaries, which includes both interslice and intraslice resource orchestrations. A secure slicing-based resource orchestration (SS-RO) algorithm is designed by minimizing the delay and resource utilization simultaneously to mitigate the impacts of the slice-initiated attacks, where the Benders decomposition is employed to obtain the interslice orchestration outcome, and a quadratic transformation method is applied to derive the intraslice orchestration solution. The experimental results demonstrate that the proposed SS-RO algorithm outperforms baseline schemes in terms of the ratio of accepted attacking tasks, energy consumption, and system throughput.
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Cloud-Network Slice (CNS) is defined as an end-to-end infrastructure composed by computing, networking, and storage resources and it is expected to be a key enabler for novel verticals such as Industry 4.0, IoT and Vehicular Netwo...
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Cloud-Network Slice (CNS) is defined as an end-to-end infrastructure composed by computing, networking, and storage resources and it is expected to be a key enabler for novel verticals such as Industry 4.0, IoT and Vehicular Networks. This paper presents the design, implementation and integration of the Architecture for Orchestration and Management of Cloud-Network Slices (CNS-AOM), a modular architecture to orchestrate and manage slice resources and services in CNSs. The CNS-AOM is designed and implemented considering three important characteristics: (ⅰ) the business model called Slice-as-a-Service (SlaaS); (ⅱ) the multiple administrative and technological domains; and (ⅲ) the slice elasticity, which means the capacity of dynamically growing and shrinking the slice resources to improve service performance. To prove the feasibility of our proposal, two Proofs of Concept (PoC) are implemented in real environments to validate the CNS-AOM. First, an end-to-end content distribution service (CDN) is deployed across three different cities in Brazil to emphasize the multiple domains. Second, we present an IoT service using a fully-featured commercial service platform called dojot, which is instantiated and orchestrated by the proposed architecture. The dojot slice is instantiated overseas in four cities across two countries. The evaluation for the CDN slice considers the appropriate metrics that should be monitored and the actual services that should be instantiated to meet the end-user's requirements depending on its location. Moreover, in the dojot slice, elasticity operations (vertical and horizontal) are tested and evaluated along with the time taken to deploy the slice infrastructure and the service. The main contributions of this paper are: (ⅰ) the design, implementation and integration of the CNS-AOM; (ⅱ) the orchestration control-loop of the slice resources; and (ⅲ) the execution of real proof-of-concept scenarios that demonstrate the feasibility of the CNS-AOM to instantiate and orchestrate services across geographically-distanced cities.
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In the coming 5G (fifth-generation mobile communications) era, it will be necessary to create diverse services using networks with high-performance characteristics such as large-capacity broadband, mas-sive session connectivity, a...
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In the coming 5G (fifth-generation mobile communications) era, it will be necessary to create diverse services using networks with high-performance characteristics such as large-capacity broadband, mas-sive session connectivity, and ultralow-latency and high-quality communications. This article introduces network slicing technology for rapidly constructing and providing virtual networks corresponding to such diverse service requirements, and slice gateway technology for achieving end-to-end slices that can maintain a fixed level of communications quality on an end-to-end basis.
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