Online Adaptive Interference-Aware VNF Deployment and Migration for 5G Network Slice

Zhang, Qixia; Liu, Fangming; Zeng, Chaobing

doi:10.1109/tnet.2021.3080197

Cited by 61 publications

(24 citation statements)

References 44 publications

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“…Resource migration also uses the same algorithm. In [44], the reward was maximised for the requested service when the user requested it. Multi-layer slicing proposed in [45] on the edge cloud was managed by 5G resources to deal with autonomous vehicles connected to Endto-End slicing networks over the transport layer.…”

Section: Literature Reviewmentioning

confidence: 99%

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

Mohammedali¹,

Kanakis²,

Al-Sherbaz³

et al. 2023

JCOMSS

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The 3G Partnership Project (3GPP) defined network slicing as a set of resources that could be scaled up and down to cover users' requirements. Machine learning and network slicing will be used together to manage and optimize resources efficiently. Sharing resources across multiple operators, such as towers, spectrum and infrastructure, can reduce the cost of 5G resources. In the proposed prototype, the end-user is connected to more than eight inter and intra-slices according to the demands. A set of slices is implemented over the 5G networks to provide an efficient service to the end-user using softwarization and virtualization technologies. Traffic is generated over multiple scenarios then End-to-End slicing traffic was analyzed after generating realtime traffic over the 5G networks. Also, all the features extracted from the traffic based on the flow behaviours and a set of elements selected from the datasets according to machine learning behaviours. Multiple machine learning algorithms are applied to our datasets using MATLAB classification application. After that, the best model is chosen to train and predict the slices using less CPU and training time to reduce the computational power in future networks and build a sustainable environment. Furthermore, the regression application predicts the slice type on the third dataset with the minimum squared error.

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Section: Literature Reviewmentioning

confidence: 99%

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

Mohammedali¹,

Kanakis²,

Al-Sherbaz³

et al. 2023

JCOMSS

View full text Add to dashboard Cite

show abstract

“…The problem of employing UAVs for expanding and complementing existing cellular infrastructures according to a 5G perspective was considered in [30], [31], [32], [33], [34], [35], [36]. However, in [33] Mozaffari et al use UAVs as flying base stations to provide a wireless backbone and give coverage to a remote area.…”

Section: Related Workmentioning

confidence: 99%

“…The new matrices created in this way are respectively saved in Ẑ (t) and Ŷ (t) . These two matrices are used to save the new optimal placement at time t derived as the solution of the optimization problem, and in particular minimizing the objective function Θ subject to constraints ( 24)- (35), given as Data of the Algorithm 1 (line 8). At the end (line 9), the MLP Algorithm returns the optimal placement solution that has to be used by UAVs composing the FANET until the next FANET modification event.…”

Section: Constraintsmentioning

confidence: 99%

“…Finally, we consider that all the chains have the same requirements in terms of maximum tolerable delay, equal to 1 ms [61], referred to the end-to-end delay in the FANET, as expressed by (35). The two weights of the objective function Θ are set as α P = 0.5 for the power consumption contribution and α G = 0.1 for the "Satisfied Flows" gain contribution.…”

Section: A Use Case Setupmentioning

confidence: 99%

“…Moreover, for networks that are based on the SDN paradigm [68], like the FANET considered in this paper, each flow re-routing is realized by a communication between the SDN Controller running in the FANET Coordinator and the SDN Switches running in the Management VF inside each UAV. This causes a transmission overhead and an additional delay that can violate the delay requirement imposed by the delay threshold described in (35). Evaluation of this impact on performance perceived by the end users, also taking into account the sensitivity of specific vertical applications to possible delay violations that can occur in the timescale of FANET event modifications, is out of scope of this paper.…”

Section: Memoryless Vs With-memory Placementmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing FANET Lifetime for 5G Softwarized Network Provisioning

Cappello

Colajanni

Daniele

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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Recently, Flying Ad Hoc Networks (FANET) have been proposed to empower 5G networks to support complex missions and provide ubiquitous connectivity to heterogeneous devices. However, it is needed to cope with the limited UAV capabilities (e.g., limited available energy to supply engines and computing elements, limited computing capabilities), as well as with the need to provide network and application services as foreseen in highly dynamic and time varying 5G ecosystems. This paper presents for the first time a comprehensive framework that integrates a FANET with a 5G network, with the aim of providing services that can be even chained with each other. This model is comprehensive in the sense that it takes into account physical constraints of the devices, as well as features and requirements of traffic flows. For this framework, the paper proposes a mathematical optimization model, allowing Virtual Function (VF) placement and chaining, aimed at minimizing energy consumption and service unsatisfaction probabilities of the FANET as a whole without employing heuristics for the solution of the problem. Two placement strategies named MLP and WMP are introduced and compared with the standard placement strategy named NoShP. An extensive numerical analysis shows that MLP and WMP allow us to well catch network dynamics and to reduce the number of virtual functions needed while decreasing the power consumption, so increasing UAV flight time and network lifetime.

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Discrete spider monkey optimization algorithm for latency‐sensitive VNF deployment and resource allocation

Liu,

Hou,

Liu

2023

Int J Communication

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SummaryThe next‐generation networks are expected to support diverse latency‐sensitive services, that is, auto‐driving, telemedicine, and so on. Deploying these services in current hardware‐based networks faces significant challenges. The emergence of network function virtualization (NFV) and software defined network (SDN) makes network deployment flexible and agile. In order to meet the strict latency constraints, joint VNF deployment and resource allocation problems are needed. In this paper, we consider the resource‐delay interpendency and formulate the joint VNF deployment and resource allocation problems as linear programming (LP) problems. Discrete spider monkey optimization (DSMO) algorithm is proposed to solve this problem, which imitates spider monkeys' fission and fusion social behavior during foraging. The two‐phase decision update mechanism greatly increases the diversity of the spider monkey decision population and promotes the algorithm to perform a global search of the solution space. Evaluation results show that the proposed solutions achieve superior performance in terms of latency, CPU utilization and service acceptance rate.

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Online Adaptive Interference-Aware VNF Deployment and Migration for 5G Network Slice

Cited by 61 publications

References 44 publications

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

Optimizing FANET Lifetime for 5G Softwarized Network Provisioning

Discrete spider monkey optimization algorithm for latency‐sensitive VNF deployment and resource allocation

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