Conference Paper

SliceNet Control Plane for 5G Network Slicing in Evolving Future Networks

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... Moreover, there is no simulation result. They extended the work in [102] to manage and control the network over multi-administrative domains to maximize the network resources that share across the network and reduce the capital expenditure. The main goal of the 5GPPP in the SliceNet platform builds E2E Network Slicing in multi-domain to optimize the capability of the QoS/QoE by using network controller style in a friendly softwarization environment. ...
... The 5GINFIRE project proposed many challenges within the management and orchestration (MANO) platform to enable network services on top of the VNFs and verticals in the 5G ecosystem. The 5GINFIRE project VOLUME 4, 2020 [102] Control plane Included Simulation for the core layer ...
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In-network softwarization, Network Slicing provides scalability and flexibility through various services such as Quality of Service (QoS) and Quality of Experience (QoE) to cover the network demands. For the QoS, a set of policies must be considered in real-time, accompanied by a group of functions and services to guarantee the end-user needs based on network demand. On the other hand, for the QoE, the service’s performance needs to be improved to bring an efficient service to cover the demands of the end-user. The 3G Partnership Project (3GPP) defined the slice as a component of resources used to process a set of packets. These resources need to be flexible, which means the resources can be scaled up or down based on the demand. This survey discusses softwarization and virtualization techniques, considering how to implement the slices for future networks. Specifically, we discuss current advances concerning the functionality and architecture of the 5G network. Therefore, the paper critically evaluates recent research and systems related to mobility management as a service in real-time inter/intra slice control by considering the strengths and limitations of these contributions to identify the research gaps and possible research directions for emerging research and development opportunities. Moreover, we extend our review by considering the slice types and their numbers based on the 3GPP Technical Specification (3GPP TS). The study presented in this paper identifies open issues and research directions that reveal that mobility management at a service level with inter/intra slice management techniques has strong potential in future networks and requires further investigation from the research community to exploit its benefits fully.
... These studies make use of the programmability of both control and data planes. Baldini et al. [31] described how the control plane of SliceNet is involved in meeting the E2E needs of different 5G verticals. Ricart-Sanchz et al. [32] proposed a QoS-aware NS framework based on hardware acceleration to support data plane programmability [32]. ...
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Through the concept of network slicing, a single physical network infrastructure can be split into multiple logically-independent Network Slices (NS), each of which is customized for the needs of its respective individual user or industrial vertical. In the beyond 5G (B5G) system, this customization can be done for many targeted services, including, but not limited to, 5G use cases and beyond 5G. The network slices should be optimized and customized to stitch a suitable environment for targeted industrial services and verticals. This paper proposes a novel Quality of Service (QoS) framework that optimizes and customizes the network slices to ensure the service level agreement (SLA) in terms of end-to-end reliability, delay, and bandwidth communication. The proposed framework makes use of network softwarization technologies, including software-defined networking (SDN) and network function virtualization (NFV), to preserve the SLA and ensure elasticity in managing the NS. This paper also mathematically models the end-to-end network by considering three parts: radio access network (RAN), transport network (TN), and core network (CN). The network is modeled in an abstract manner based on these three parts. Finally, we develop a prototype system to implement these algorithms using the open network operating system (ONOS) as a SDN controller. Simulations are conducted using the Mininet simulator. The results show that our QoS framework and the proposed resource allocation algorithms can effectively schedule network resources for various NS types and provide reliable E2E QoS services to end-users.
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6G is expected to support the unprecedented Internet of everything scenarios with extremely diverse and challenging requirements. To fulfill such diverse requirements efficiently, 6G is envisioned to be space-aerial-terrestrial-ocean integrated three-dimension networks with different types of slices enabled by new technologies and paradigms to make the system more intelligent and flexible. As 6G networks are increasingly complex, heterogeneous and dynamic, it is very challenging to achieve efficient resource utilization, seamless user experience, automatic management and orchestration. With the advancement of big data processing technology, computing power and the availability of rich data, it is natural to tackle complex 6G network issues by leveraging artificial intelligence (AI). In this paper, we make a comprehensive survey about AI-empowered networks evolving towards 6G. We first present the vision of AI-enabled 6G system, the driving forces of introducing AI into 6G and the state of the art in machine learning. Then applying machine learning techniques to major 6G network issues including advanced radio interface, intelligent traffic control, security protection, management and orchestration, and network optimization is extensively discussed. Moreover, the latest progress of major standardization initiatives and industry research programs on applying machine learning to mobile networks evolving towards 6G are reviewed. Finally, we identify important open issues to inspire further studies towards an intelligent, efficient and secure 6G system.
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Control-plane operations are indispensable to providing data access to mobile devices in the 4G LTE networks. They provision necessary control states at the device and network nodes to enable data access. However, the current design may suffer from long data access latency even under good radio conditions. The fundamental problem is that, data-plane packet delivery cannot start or resume until all control-plane procedures are completed, and these control procedures run sequentially by design. We show both are more than necessary under popular use cases. We design DPCM, which reduces data access latency through parallel processing approaches and exploiting device-side state replica. We implement DPCM and validate its effectiveness with extensive evaluations.
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In order to meet the increasing demands of high data rate and low latency cellular broadband applications, plans are underway to roll out the Fifth Generation (5G) cellular wireless system by the year 2020. This paper proposes a novel method for adapting the Third Generation Partnership Project (3GPP)'s 5G architecture to the principles of Software Defined Networking (SDN). We propose to have centralized network functions in the 5G network core to control the network, end-to-end. This is achieved by relocating the control functionality present in the 5G Radio Access Network (RAN) to the network core, resulting in the conversion of the base station known as the gNB into a pure data plane node. This brings about a significant reduction in signaling costs between the RAN and the core network. It also results in improved system performance. The merits of our proposal have been illustrated by evaluating the Key Performance Indicators (KPIs) of the 5G network, such as network attach (registration) time and handover time. We have also demonstrated improvements in attach time and system throughput due to the use of centralized algorithms for mobility management with the help of ns-3 simulations.
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Cellular networks play a dominant role in how we communicate. But, the current cellular architecture and protocols are overly complex. The 'control plane' protocol includes setting up explicit tunnels for every session and exchanging a large number of packets among the different entities (mobile device, base station, the packet gateways and mobility management) to ensure state is exchanged in a consistent manner. This limits scalability. As we evolve to having to support an increasing number of users, cell-sites (e.g., 5G) and the consequent mobility, and the incoming wave of IoT devices, a re-thinking of the architecture and control protocols is required. In this work we propose CleanG, a simplified software-based architecture for the Mobile Core Network (MCN) and a simplified control protocol for cellular networks. Network Function Virtualization enables dynamic management of capacity in the cloud to support the MCN of future cellular networks. We develop a simplified protocol that substantially reduces the number of control messages exchanged to support the various events, while retaining the current functionality expected from the network. CleanG, we believe will scale better and have lower latency.
Conference Paper
Although the radio access network (RAN) part of mobile networks offers a significant opportunity for benefiting from the use of SDN ideas, this opportunity is largely untapped due to the lack of a software-defined RAN (SD-RAN) platform. We fill this void with FlexRAN, a flexible and programmable SD-RAN platform that separates the RAN control and data planes through a new, custom-tailored southbound API. Aided by virtualized control functions and control delegation features, FlexRAN provides a flexible control plane designed with support for real-time RAN control applications, flexibility to realize various degrees of coordination among RAN infrastructure entities, and programmability to adapt control over time and easier evolution to the future following SDN/NFV principles. We implement FlexRAN as an extension to a modified version of the OpenAirInterface LTE platform, with evaluation results indicating the feasibility of using FlexRAN under the stringent time constraints posed by the RAN. To demonstrate the effectiveness of FlexRAN as an SD-RAN platform and highlight its applicability for a diverse set of use cases, we present three network services deployed over FlexRAN focusing on interference management, mobile edge computing and RAN sharing.
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The tremendous growth in wireless Internet use is showing no signs of slowing down. Existing cellular networks are starting to be insufficient in meeting this demand, in part due to their inflexible and expensive equipment as well as complex and non-agile control plane. Software-defined networking is emerging as a natural solution for next generation cellular networks as it enables further network function virtualization opportunities and network programmability. In this article, we advocate an all-SDN network architecture with hierarchical network control capabilities to allow for different grades of performance and complexity in offering core network services and provide service differentiation for 5G systems. As a showcase of this architecture, we introduce a unified approach to mobility, handoff, and routing management and offer connectivity management as a service (CMaaS). CMaaS is offered to application developers and over-the-top service providers to provide a range of options in protecting their flows against subscriber mobility at different price levels.
Technology Independent Information Model for Network Slicing
  • L Qiang
2083-0, IMT Vision -Framework and overall objectives of the future development of IMT for 2020 and beyond
  • Itu-R M Recommendation
Recommendation ITU-R M.2083-0, IMT Vision -Framework and overall objectives of the future development of IMT for 2020 and beyond, Sept. 2015.
IETF COMS I-D, draft-qiang-coms-netslicing-information-model-02
  • L Qiang
L. Qiang et al., "Technology Independent Information Model for Network Slicing", IETF COMS I-D, draft-qiang-coms-netslicing-information-model-02, work in progress.
Available at https://selfnet-5g
  • Selfnet Project
SELFNET Project, [Online]. Available at https://selfnet-5g.eu/ [11] CogNet Project, [Online].