Probabilistic-Forecasting-Based Admission Control for Network Slicing in Software-Defined Networks

Jiang, Weiwei; Zhan, Yafeng; Zeng, Guanming; Lu, Jianhua

doi:10.1109/jiot.2022.3145475

Cited by 21 publications

(9 citation statements)

References 48 publications

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“…The control layer is responsible for controlling and managing the infrastructure layer. And the application layer provides network services and business requirements [5]. In the SDN architecture, infrastructure, controllers and applications are not limited to existing software, hardware, virtualization or physical form.…”

Section: Software Defined Networkingmentioning

confidence: 99%

Insight Into the Architecture and Technology of Smart Identifier Networks

Fan

2024

Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–

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The traditional Internet adopts a host-centric data transmission mode, and data are repeatedly transmitted between different computers, resulting in vast network traffic and low transmission efficiency. With the rise of the information-centric content sending mode, the traditional Internet has been unable to meet growing business needs. In this context, smart identifier networks have been proposed as next-generation Internet architectures and were initially applied in the industrial Internet, Internet of things and other fields. As the research direction of future network architecture, smart identifier networks are currently mainly deployed in the field of industrial manufacturing. It still needs to explore efficient applications on the Internet of Vehicles, Space-Earth Integration Networks, data centers and other scenarios to meet the needs of current new business in the short term, fundamentally solve the shortage of network architecture in the long term and supply a good development environment for future identification applications.

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Section: Software Defined Networkingmentioning

confidence: 99%

Insight Into the Architecture and Technology of Smart Identifier Networks

Fan

2024

Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–

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“…It not only can realize point forecasting and interval forecasting, but also make the both simultaneously work. Therefore, it is widely used in traffic forecasting [22], sales forecasting and energy forecasting [23] and other fields. In addition, the predicted value provided by the DeepAR model is not just a specific value, but a probability distribution, which is used to describe the possible range of the data point.…”

Section: Deeparmentioning

confidence: 99%

Intelligent prediction of rolling bearing remaining useful life based on probabilistic DeepAR-Transformer model

Deng,

Li,

Zhang

2023

Meas. Sci. Technol.

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Remaining useful life (RUL) prediction for rolling bearings requires highly accurate and stable long-term prediction capabilities in equipment health management, which demands that the prediction model has strong data reasoning and regression performance. However, it is difficult to accurately capture long-term dependencies via traditional convolutional neural network because the information loss and insufficient analysis are unavoidable during the feature extraction process. An end-to-end time series forecasting method called D-former for RUL prediction of rolling bearings is proposed in this paper. The method mainly consists of DeepAR and a multi-layer encoder, so it is able to extract degradation features directly from the original signal. This method has the following salient features: (1) the designed multi-head attention mechanism can highlight important feature information and realize parallel computing, so the method is extremely suitable for processing long-term time series; (2) the important time feature information is rearranged through DeepAR, so the method has the excellent domain adaptability, and it can achieve accurate prediction of rolling bearing RUL under different working conditions. The verification experiment was implemented on the IEEE PHM 2012 dataset and the XJTU-SY bearing dataset. The experimental results show that the proposed D-former method is actually superior to the existing mainstream RUL prediction methods.

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“…The work in [25] uses probabilistic forecasting based DeepAR to predict slice resources from a real dataset and admits slices based on resource availability. In [20], two slice classes, namely Best Effort and Guaranteed Service are considered.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The drawbacks of the these approaches are that some consider effective resource utilization with QoS control, and some consider intelligent admission for revenue management, but none consider both. Also, none of the mentioned works use real data except [25]. However, it uses hard coding to admit slices concerning slice priorities and only focus on prediction techniques.…”

Section: Background and Related Workmentioning

confidence: 99%

DRL-Based Slice Admission Using Overbooking in 5G Networks

Saxena

Sivalingam

2023

IEEE Open J. Commun. Soc.

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In 5G-and-beyond networks, the concept of Network Slicing is used to support multiple independent and co-existing logical networks on a physical network infrastructure. The infrastructure provider (InP) owns the set of virtual and physical resources that are used to support the tenant slice requests. Each slice request specifies a service level agreement (SLA) that contains the required slice-level resources (computation and communication) and the revenue provided by the tenant. Due to limited resources, the InP cannot accommodate all requests made by the tenants. In general, it has been found that tenants tend to overestimate their resource demands (e.g. for 5G Core computation) to reduce possible SLA violations. In this paper, we consider two major slice types: Elastic (low priority and low revenue) and Inelastic (high priority and high revenue). We apply the concept of overbooking, where the InP accepts more slices while considering slice priorities in order to maximize the overall revenue and utilization. We consider a multi-tenant environment and propose a slice admission system named PRLOV, which is based on Reinforcement Learning (RL) and prediction methods. The system predicts the future resource demands of Elastic slices and applies an opportunistic overbooking technique to overbook InP for accepting more slices. In addition, the admission decision is formulated as an Markov Decision Process (MDP) problem and solved using standard RL techniques (Policy Iteration, Q-Learning, DQN). The performance of our proposed work is compared against three other heuristics (Basic, Prediction, PRL) that do not use overbooking. Data traces from the Materna data center networks were used for prediction purposes. The important performance metrics measures include InP total revenue, the acceptance rate of respective slices and overall resource utilization for different slices. The results show that the proposed work significantly outperforms the other mechanisms in terms of revenue gain and resource utilization. Simulation results show that PRLOV provides a revenue gain of 6%, 26%, and 102% compared to the PRL, Prediction and Basic scheme.

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Probabilistic-Forecasting-Based Admission Control for Network Slicing in Software-Defined Networks

Cited by 21 publications

References 48 publications

Insight Into the Architecture and Technology of Smart Identifier Networks

Insight Into the Architecture and Technology of Smart Identifier Networks

Intelligent prediction of rolling bearing remaining useful life based on probabilistic DeepAR-Transformer model

DRL-Based Slice Admission Using Overbooking in 5G Networks

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