Towards 5G network slicing for vehicular ad-hoc networks: An end-to-end approach

Afaq, Muhammad; Iqbal, Javed; Ahmed, Talha; Islam, Ihtesham Ul; Khan, Murad; Khan, Muhammad Sohail

doi:10.1016/j.comcom.2019.10.018

Cited by 39 publications

(20 citation statements)

References 6 publications

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“…As it is a closed-loop architecture, the second part is to continuously monitor the orchestrated resources and update the system for optimal resource configuration. Hence, perceiving the current state of the system and projecting the system status to the NN model, the future state of the system is determined, which proactively updates the system configurations to assure service provisioning [25][26][27][28][29][30][31][32][33].…”

Section: System Architecture and Workingmentioning

confidence: 99%

An Intent-Driven Closed-Loop Platform for 5G Network Service Orchestration

Khan¹,

Abbas²,

Afaq³

et al. 2022

Computers, Materials &Amp; Continua

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The scope of the 5G network is not only limited to the enhancements in the form of the quality of service (QoS), but it also includes a wide range of services with various requirements. Besides this, many approaches and platforms are under the umbrella of 5G to achieve the goals of endto-end service provisioning. However, the management of multiple services over heterogeneous platforms is a complex task. Each platform and service have various requirements to be handled by domain experts. Still, if the next-generation network management is dependent on manual updates, it will become impossible to provide seamless service provisioning in runtime. Since the traffic for a particular type of service varies significantly over time, automatic provisioning of resources and orchestration in runtime need to be integrated. Besides, with the increase in the number of devices, amount, and variety of traffic, the management of resources with optimization becomes a challenging task. To this end, this manuscript provides a solution that automates the management and service provisioning through multiple platforms while assuring various aspects, including automation, resource management and service assurance. The solution consists of an intent-based system that automatically manages different orchestrators, and eliminates manual control by abstracting the complex configuration requirements into simple and generic contracts. The proposed system considers handling the scalability of resources in runtime by using Machine Learning (ML) to automate and optimize service resource utilization.

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Section: System Architecture and Workingmentioning

confidence: 99%

An Intent-Driven Closed-Loop Platform for 5G Network Service Orchestration

Khan¹,

Abbas²,

Afaq³

et al. 2022

Computers, Materials &Amp; Continua

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“…Many open-source solutions enable the NOs to perform e2e network slicing developed by different organizations, including ONAP, OSM, OpenBaton, JOX, Cloudify, Tacker, 5G NORMA, M-CORD, 5G TANGO, SONATA, 5G-PAGODA, OpenNFV, T-NOVA, OPNFV [4], [18], [20], [22]- [29].…”

Section: A Network Slicing Progress and Standardsmentioning

confidence: 99%

Network Slice Lifecycle Management for 5G Mobile Networks: An Intent-Based Networking Approach

et al. 2021

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Network slicing in 5G is a solution to accommodate a wide range of services. It also enables the network operators to establish multiple end-to-end (e2e) logically isolated and customized networks with shared or dedicated resources over the same infrastructure. Although, many tools and platforms have been developed to accomplish the management and orchestration (MANO) of e2e network slicing automatically, it is still challenging. Each of these platforms requires expertise and manual effort to define the requirements for the provisioning of the resources. The other issue is the generation of well-defined network slice configurations with lifecycle parameters. To this end, this paper proposes an efficient solution that automates the configuration process and performs the management and orchestration of network slices. This solution contains a one-touch Intent-based Networking (IBN) platform that effectively orchestrates and manages the lifecycle of multi-domain slice resources. IBN automates the process of slice configuration generation, service provisioning, service update, and service assurance by eliminating experts and manual effort. Furthermore, it has an intelligent Deep Learning (DL) based resource update and assurance mechanism which handles the run-time resource scalability and assurance.

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“…In addition, the AHP cannot support multiple traffic flow resource allocation concurrently. A dynamic end-to-end slicing approach has been used to test the vehicular ad hoc network environment with support for 5G communications [35]; two types of slice services are tested: Video and Web. In a single physical network infrastructure, resources are allocated for network slices.…”

Section: B Network Slicing and Resource Allocationmentioning

confidence: 99%

T-S3RA: Traffic-Aware Scheduling for Secure Slicing and Resource Allocation in SDN/NFVEnabled 5G Networks

Ramadhan¹

2021

IJETT

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Network slicing and resource allocation play pivotal roles in software-defined network (SDN)/network function virtualization (NFV)-assisted 5G networks. In 5G communications, the traffic rate is high, necessitating high data rates and low latency. Deep learning is a potential solution for overcoming these constraints. Secure slicing avoids resource wastage; however, DDoS attackers can exploit the sliced network. Therefore, we focused on secure slicing with resource allocation under massive network traffic. Traffic-aware scheduling is proposed for secure slicing and resource allocation over SDN/NFV-enabled 5G networks. In this approach (T-S3RA), user devices are authenticated using Boolean logic with a password-based key derivation function. The traffic is scheduled in 5G access points, and secure network slicing and resource allocation are implemented using deep learning models such as SliceNet and HopFieldNet, respectively. To predict DDoS attackers, we computed the Renyi entropy for packet classification. Experiments were conducted using a network simulator with 250 nodes in the network topology. Performance was evaluated using metrics such as throughput, latency, packet transmission ratio, packet loss ratio, slice capacity, bandwidth consumption, and slice acceptance ratio. T-S3RA was implemented in three 5G use cases with different requirements, including massive machine-type communication, ultra-reliable low-latency communication, and enhanced mobile broadband.

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Towards 5G network slicing for vehicular ad-hoc networks: An end-to-end approach

Cited by 39 publications

References 6 publications

An Intent-Driven Closed-Loop Platform for 5G Network Service Orchestration

An Intent-Driven Closed-Loop Platform for 5G Network Service Orchestration

Network Slice Lifecycle Management for 5G Mobile Networks: An Intent-Based Networking Approach

T-S3RA: Traffic-Aware Scheduling for Secure Slicing and Resource Allocation in SDN/NFVEnabled 5G Networks

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