Journal articles: 'Gestion de Network Slice'

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Chevalier, Marc, Rafaël De Sa, Laura Cardoit, and Muriel Thoby-Brisson. "Mechanisms Underlying Adaptation of Respiratory Network Activity to Modulatory Stimuli in the Mouse Embryo." Neural Plasticity 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3905257.

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Breathing is a rhythmic behavior that requires organized contractions of respiratory effector muscles. This behavior must adapt to constantly changing conditions in order to ensure homeostasis, proper body oxygenation, and CO2/pH regulation. Respiratory rhythmogenesis is controlled by neural networks located in the brainstem. One area considered to be essential for generating the inspiratory phase of the respiratory rhythm is the preBötzinger complex (preBötC). Rhythmogenesis emerges from this network through the interplay between the activation of intrinsic cellular properties (pacemaker properties) and intercellular synaptic connections. Respiratory activity continuously changes under the impact of numerous modulatory substances depending on organismal needs and environmental conditions. The preBötC network has been shown to become active during the last third of gestation. But only little is known regarding the modulation of inspiratory rhythmicity at embryonic stages and even less on a possible role of pacemaker neurons in this functional flexibility during the prenatal period. By combining electrophysiology and calcium imaging performed on embryonic brainstem slice preparations, we provide evidence showing that embryonic inspiratory pacemaker neurons are already intrinsically sensitive to neuromodulation and external conditions (i.e., temperature) affecting respiratory network activity, suggesting a potential role of pacemaker neurons in mediating rhythm adaptation to modulatory stimuli in the embryo.

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Wong, Stan, Bin Han, and Hans D. Schotten. "5G Network Slice Isolation." Network 2, no. 1 (March 8, 2022): 153–67. http://dx.doi.org/10.3390/network2010011.

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This article reveals an adequate comprehension of basic defense, security challenges, and attack vectors in deploying multi-network slicing. Network slicing is a revolutionary concept of providing mobile network on-demand and expanding mobile networking business and services to a new era. The new business paradigm and service opportunities are encouraging vertical industries to join and develop their own mobile network capabilities for enhanced performances that are coherent with their applications. However, a number of security concerns are also raised in this new era. In this article, we focus on the deployment of multi-network slicing with multi-tenancy. We identify the security concerns and discuss the defense approaches such as network slice isolation and insulation in a multi-layer network slicing security model. Furthermore, we identify the importance to appropriately select the network slice isolation points and propose a generic framework to optimize the isolation policy regarding the implementation cost while guaranteeing the security and performance requirements.

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Ping, Jing. "Network Resource Model for 5G Networkand Network Slice." Journal of ICT Standardization 7, no. 2 (2019): 127–40. http://dx.doi.org/10.13052/jicts2245-800x.7234.

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Ping, Jing. "Network Resource Model for 5G Networkand Network Slice." Journal of ICT Standardization 7, no. 2 (2019): 127–40. http://dx.doi.org/10.13052/jicts2245-800x.724.

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Kulmar, Marika, Ivo Müürsepp, and Muhammad Mahtab Alam. "Heuristic Radio Access Network Subslicing with User Clustering and Bandwidth Subpartitioning." Sensors 23, no. 10 (May 10, 2023): 4613. http://dx.doi.org/10.3390/s23104613.

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In 5G and beyond, the network slicing is a crucial feature that ensures the fulfillment of service requirements. Nevertheless, the impact of the number of slices and slice size on the radio access network (RAN) slice performance has not yet been studied. This research is needed to understand the effects of creating subslices on slice resources to serve slice users and how the performance of RAN slices is affected by the number and size of these subslices. A slice is divided into numbers of subslices of different sizes, and the slice performance is evaluated based on the slice bandwidth utilization and slice goodput. A proposed subslicing algorithm is compared with k-means UE clustering and equal UE grouping. The MATLAB simulation results show that subslicing can improve slice performance. If the slice contains all UEs with a good block error ratio (BLER), then a slice performance improvement of up to 37% can be achieved, and it comes more from the decrease in bandwidth utilization than the increase in goodput. If a slice contains UEs with a poor BLER, then the slice performance can be improved by up to 84%, and it comes only from the goodput increase. The most important criterion in subslicing is the minimum subslice size in terms of resource blocks (RB), which is 73 for a slice that contains all good-BLER UEs. If a slice contains UEs with poor BLER, then the subslice can be smaller.

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Li, Xin, Chengcheng Guo, Jun Xu, Lav Gupta, and Raj Jain. "Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes." Applied Sciences 9, no. 20 (October 16, 2019): 4361. http://dx.doi.org/10.3390/app9204361.

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Efficient provisioning of 5G network slices is a major challenge for 5G network slicing technology. Previous slice provisioning methods have only considered network resource attributes and ignored network topology attributes. These methods may result in a decrease in the slice acceptance ratio and the slice provisioning revenue. To address these issues, we propose a two-stage heuristic slice provisioning algorithm, called RT-CSP, for the 5G core network by jointly considering network resource attributes and topology attributes in this paper. The first stage of our method is called the slice node provisioning stage, in which we propose an approach to scoring and ranking nodes using network resource attributes (i.e., CPU capacity and bandwidth) and topology attributes (i.e., degree centrality and closeness centrality). Slice nodes are then provisioned according to the node ranking results. In the second stage, called the slice link provisioning stage, the k-shortest path algorithm is implemented to provision slice links. To further improve the performance of RT-CSP, we propose RT-CSP+, which uses our designed strategy, called minMaxBWUtilHops, to select the best physical path to host the slice link. The strategy minimizes the product of the maximum link bandwidth utilization of the candidate physical path and the number of hops in it to avoid creating bottlenecks in the physical path and reduce the bandwidth cost. Using extensive simulations, we compared our results with those of the state-of-the-art algorithms. The experimental results show that our algorithms increase slice acceptance ratio and improve the provisioning revenue-to-cost ratio.

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Walia, Jaspreet Singh, Heikki Hämmäinen, Kalevi Kilkki, Hannu Flinck, Seppo Yrjölä, and Marja Matinmikko-Blue. "A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory." Journal of Sensor and Actuator Networks 10, no. 3 (July 28, 2021): 51. http://dx.doi.org/10.3390/jsan10030051.

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Network slicing is a key enabler for providing new services to industry verticals. In order to enable network slice provisioning, it is important to study the network slice type allocation for different device types in a real industrial case. Furthermore, the costs of the required virtualization infrastructure need to be analyzed for various cloud deployment scenarios. In this paper, a cost model for the virtualization infrastructure needed for network slice provisioning is developed and subsequently applied to a real smart factory. In the model, slice types and devices are mapped such that each factory device is provisioned with one or more slice types, as required. The number of devices to be supported per slice type is forecasted for 2021–2030, and the total costs of ownership, costs per slice type, and costs for every slice type, for each device are calculated. The results are analyzed for three cloud deployment scenarios: local, distributed, and centralized. The centralized scenario was found to have the lowest cost. Moreover, sensitivity analysis is conducted by varying the device growth, the number of factories, the level of isolation between network slices, and resource overbooking. The resulting evaluation and cost breakdown can help stakeholders select a suitable deployment scenario, gauge their investments, and exercise suitable pricing.

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Zhou, Jinhe, Wenjun Zhao, and Shuo Chen. "Dynamic Network Slice Scaling Assisted by Prediction in 5G Network." IEEE Access 8 (2020): 133700–133712. http://dx.doi.org/10.1109/access.2020.3010623.

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Walia, Jaspreet Singh, Heikki Hämmäinen, Kalevi Kilkki, Hannu Flinck, Marja Matinmikko-Blue, and Seppo Yrjölä. "Network Slice Provisioning Approaches for Industry Verticals." International Journal of Business Data Communications and Networking 17, no. 2 (July 2021): 1–15. http://dx.doi.org/10.4018/ijbdcn.286700.

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Network slicing is widely studied as an essential technological enabler for supporting diverse use case specific services through network virtualization. Industry verticals, consisting of diverse use cases requiring different network resources, are considered key customers for network slices. However, different approaches for network slice provisioning to industry verticals and required business models are still largely unexplored and require further work. Focusing on technical and business aspects of network slicing, this article develops three new business models, enabled by different distributions of business roles and management exposure between business actors. The feasibility of the business models is studied in terms of; the costs and benefits to business actors, mapping to use cases in various industry verticals, and the infrastructure costs of common and dedicated virtualization infrastructures. Finally, a strategic approach and relevant recommendations are proposed for major business actors, national regulatory authorities, and standards developing organizations.

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Wang, Gang, Gang Feng, Shuang Qin, Ruihan Wen, and Sanshan Sun. "Optimizing Network Slice Dimensioning via Resource Pricing." IEEE Access 7 (2019): 30331–43. http://dx.doi.org/10.1109/access.2019.2902432.

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Pazhani.A, Azhagu Jaisudhan, P. Gunasekaran, Vimal Shanmuganathan, Sangsoon Lim, Kaliappan Madasamy, Rajesh Manoharan, and Amit Verma. "Peer–Peer Communication Using Novel Slice Handover Algorithm for 5G Wireless Networks." Journal of Sensor and Actuator Networks 11, no. 4 (November 29, 2022): 82. http://dx.doi.org/10.3390/jsan11040082.

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The goal of 5G wireless networks is to address the growing need for network services among users. User equipment has progressed to the point where users now expect diverse services from the network. The latency, reliability, and bandwidth requirements of users can all be classified. To fulfil the different needs of users in an economical manner, while guaranteeing network resources are resourcefully assigned to consumers, 5G systems plan to leverage technologies like Software Defined Networks, Network Function Virtualization, and Network Slicing. For the purpose of ensuring continuous handover among network slices, while catering to the advent of varied 5G application scenarios, new mobility management techniques must be adopted in Sliced 5G networks. Users want to travel from one region of coverage to another region without any fading in their network connection. Different network slices can coexist in 5G networks, with every slice offering services customized to various QoS demands. As a result, when customers travel from one region of coverage to another, the call can be transferred to a slice that caters to similar or slightly different requirements. The goal of this study was to develop an intra- and inter-slice algorithm for determining handover decisions in sliced 5G networks and to assess performance by comparing intra- and inter-slice handovers. The proposed work shows that an inter-slice handover algorithm offers superior quality of service when compared to an intra-slice algorithm.

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Wang, Qian, Yanan Zhang, and Xuanzhong Wang. "Resource Allocation Optimization Algorithm of Power 5G Network Slice Based on NFV and SDN." Journal of Physics: Conference Series 2476, no. 1 (April 1, 2023): 012085. http://dx.doi.org/10.1088/1742-6596/2476/1/012085.

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Abstract To realize 5G network slicing, the virtual resource scheduling and allocation method based on NFV and SDN is designed. The scheme comprehensively considers 5G communication technology and network slice structure, and establishes the optimal mathematical model of network slice operation economy. Then the network functions and resources are virtualized by NFV and SDN, the greedy algorithm is adopted to solve the mapping problem of network slices, and the specific implementation algorithm is provided. The simulation results show that the allocation algorithm can customize the management of network slice resources, achieve flexible control and sharing of network traffic and resources, and significantly promote the construction, optimization and promotion of 5G mobile communication network architecture.

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Gui, Haitian, Tao Su, Zhiyong Pang, Han Jiao, Lang Xiong, Xinhua Jiang, Li Li, and Zixin Wang. "Diagnosis of Breast Cancer with Strongly Supervised Deep Learning Neural Network." Electronics 11, no. 19 (September 22, 2022): 3003. http://dx.doi.org/10.3390/electronics11193003.

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The strongly supervised deep convolutional neural network (DCNN) has better performance in assessing breast cancer (BC) because of the more accurate features from the slice-level precise labeling compared with the image-level labeling weakly supervised DCNN. However, manual slice-level precise labeling is time consuming and expensive. In addition, the slice-level diagnosis adopted in the DCNN system is incomplete and defective because of the lack of other slices’ information. In this paper, we studied the impact of the region of interest (ROI) and lesion-level multi-slice diagnosis in the DCNN auxiliary diagnosis system. Firstly, we proposed an improved region-growing algorithm to generate slice-level precise ROI. Secondly, we adopted the average weighting method as the lesion-level diagnosis criteria after exploring four different weighting methods. Finally, we proposed our complete system, which combined the densely connected convolutional network (DenseNet) with the slice-level ROI and the average weighting lesion-level diagnosis after evaluating the performance of five DCNNs. The proposed system achieved an AUC of 0.958, an accuracy of 92.5%, a sensitivity of 95.0%, and a specificity of 90.0%. The experimental results showed that our proposed system had a better performance in BC diagnosis because of the more precise ROI and more complete information of multi-slices.

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Samdanis, Konstantinos, Xavier Costa-Perez, and Vincenzo Sciancalepore. "From network sharing to multi-tenancy: The 5G network slice broker." IEEE Communications Magazine 54, no. 7 (July 2016): 32–39. http://dx.doi.org/10.1109/mcom.2016.7514161.

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An, Namwon, Yonggang Kim, Juman Park, Dae-Hoon Kwon, and Hyuk Lim. "Slice Management for Quality of Service Differentiation in Wireless Network Slicing." Sensors 19, no. 12 (June 19, 2019): 2745. http://dx.doi.org/10.3390/s19122745.

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Network slicing is a technology that virtualizes a single infrastructure into multiple logical networks (called slices) where resources or virtualized functions can be flexibly configured by demands of applications to satisfy their quality of service (QoS) requirements. Generally, to provide the guaranteed QoS in applications, resources of slices are isolated. In wired networks, this resource isolation is enabled by allocating dedicated data bandwidths to slices. However, in wireless networks, resource isolation may be challenging because the interference between links affects the actual bandwidths of slices and degrades their QoS. In this paper, we propose a slice management scheme that mitigates the interference imposed on each slice according to their priorities by determining routes of flows with a different routing policy. Traffic flows in the slice with the highest priority are routed into shortest paths. In each lower-priority slice, the routing of traffic flows is conducted while minimizing a weighted summation of interference to other slices. Since higher-priority slices have higher interference weights, they receive lower interference from other slices. As a result, the QoS of slices is differentiated according to their priorities while the interference imposed on slices is reduced. We compared the proposed slice management scheme with a naïve slice management (NSM) method that differentiates QoS among slices by priority queuing. We conducted some simulations and the simulation results show that our proposed management scheme not only differentiates the QoS of slices according to their priorities but also enhances the average throughput and delay performance of slices remarkably compared to that of the NSM method. The simulations were conducted in grid network topologies with 16 and 100 nodes and a random network topology with 200 nodes. Simulation results indicate that the proposed slice management increased the average throughput of slices up to 6%, 13%, and 7% and reduced the average delay of slices up to 14%, 15%, and 11% in comparison with the NSM method.

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Sunday Oladayo Oladejo, Stephen Obono Ekwe, and Lateef Adesola Akinyemi. "Multi-tier multi-tenant network slicing: A multi-domain games approach." ITU Journal on Future and Evolving Technologies 2, no. 6 (September 23, 2021): 57–82. http://dx.doi.org/10.52953/dxzq6155.

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The 5G slice networks will play a critical role in meeting the stringent quality-of-service requirements of different use cases, reducing the Capital Expenditure (CapEX) and Operational Expenditure (OpEX) of mobile network operators. Owing to the flexibility and ability of 5G slice networks to meet the needs of different verticals, it attracts new network players and entities to the mobile network ecosystem, and therefore it creates new business models and structures. Motivated by this development, this paper addresses the dynamic resource allocation in a multi-slice multi-tier multi-domain network with different network players. The dynamic resource allocation problem is formulated as a maximum utility optimisation problem from a multiplayer multi-domain perspective. Furthermore, a 3-level hierarchical business model comprising Infrastructure Providers (InPs), Mobile Virtual Network Operators (MVNOs), Service Providers (SPs), and slice users are investigated. We propose two schemes: a multi-tier multi-domain slice user matching game scheme and a distributed backtracking multiplayer multi-domain game scheme in solving the transformed maximum utility optimisation problem. We compare the multi-tier multi-tenant multi-domain game scheme with a Genetic Algorithm (GA) Intelligent Latency-Aware Resource (GI-LARE) allocation scheme, and a static slicing resource allocation scheme via Monte Carlo simulation. Our findings reveal that the proposed scheme significantly outperforms these other schemes.

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Gabilondo, Álvaro, Zaloa Fernández, Roberto Viola, Ángel Martín, Mikel Zorrilla, Pablo Angueira, and Jon Montalbán. "Traffic Classification for Network Slicing in Mobile Networks." Electronics 11, no. 7 (March 30, 2022): 1097. http://dx.doi.org/10.3390/electronics11071097.

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Network slicing is a promising technique used in the smart delivery of traffic and can satisfy the requirements of specific applications or systems based on the features of the 5G network. To this end, an appropriate slice needs to be selected for each data flow to efficiently transmit data for different applications and heterogeneous requirements. To apply the slicing paradigm at the radio segment of a cellular network, this paper presents two approaches for dynamically classifying the traffic types of individual flows and transmitting them through a specific slice with an associated 5G quality-of-service identifier (5QI). Finally, using a 5G standalone (SA) experimental network solution, we apply the radio resource sharing configuration to prioritize traffic that is dispatched through the most suitable slice. The results demonstrate that the use of network slicing allows for higher efficiency and reliability for the most critical data in terms of packet loss or jitter.

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Ren, Zhe, Xinghua Li, Qi Jiang, Qingfeng Cheng, and Jianfeng Ma. "Fast and Universal Inter-Slice Handover Authentication with Privacy Protection in 5G Network." Security and Communication Networks 2021 (January 31, 2021): 1–19. http://dx.doi.org/10.1155/2021/6694058.

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In a 5G network-sliced environment, mobility management introduces a new form of handover called inter-slice handover among network slices. Users can change their slices as their preferences or requirements vary over time. However, existing handover-authentication mechanisms cannot support inter-slice handover because of the fine-grained demand among network slice services, which could cause challenging issues, such as the compromise of service quality, anonymity, and universality. In this paper, we address these issues by introducing a fast and universal inter-slice (FUIS) handover authentication framework based on blockchain, chameleon hash, and ring signature. To address these issues, we introduce an anonymous service-oriented authentication protocol with a key agreement for inter-slice handover by constructing an anonymous ticket with the trapdoor collision property of chameleon hash functions. In order to reduce the computation overhead of the user side in the process of authentication, a privacy-preserving ticket validation with a ring signature is designed to finish in the consensus phase of the blockchain in advance. Thanks to the edge computing capabilities in 5G, distributed edge nodes help to store the anonymous ticket information, which guarantees that the legal users can finish authentication swiftly during handover. Our scheme's performance is evaluated through simulation experiments to testify the efficiency and feasibility in a 5G network-sliced environment. The results show that compared to other authentication schemes of the same type, the overall inter-slice handover delay has been reduced by 97.94%.

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Wang, Yadong, Guohua Wang, Bo Yang, Haijun Tao, Jack Y. Yang, Youping Deng, and Yunlong Liu. "Reconstruct gene regulatory network using slice pattern model." BMC Genomics 10, Suppl 1 (2009): S2. http://dx.doi.org/10.1186/1471-2164-10-s1-s2.

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Sun, Xiao-Qian, Hua-Wei Shen, Xue-Qi Cheng, and Yuqing Zhang. "Detecting anomalous traders using multi-slice network analysis." Physica A: Statistical Mechanics and its Applications 473 (May 2017): 1–9. http://dx.doi.org/10.1016/j.physa.2016.12.052.

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Chen, Dongming, Mingshuo Nie, Jie Wang, Yun Kong, Dongqi Wang, and Xinyu Huang. "Community Detection Based on Graph Representation Learning in Evolutionary Networks." Applied Sciences 11, no. 10 (May 14, 2021): 4497. http://dx.doi.org/10.3390/app11104497.

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Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

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Liu, Xian, Jian Lu, Zeyang Cheng, and Xiaochi Ma. "A Dynamic Bayesian Network-Based Real-Time Crash Prediction Model for Urban Elevated Expressway." Journal of Advanced Transportation 2021 (May 13, 2021): 1–12. http://dx.doi.org/10.1155/2021/5569143.

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Traffic crash is a complex phenomenon that involves coupling interdependency among multiple influencing factors. Considering that interdependency is critical for predicting crash risk accurately and contributes to revealing the underlying mechanism of crash occurrence as well, the present study attempts to build a Real-Time Crash Prediction Model (RTCPM) for urban elevated expressway accounting for the dynamicity and coupling interdependency among traffic flow characteristics before crash occurrence and identify the most probable risk propagation path and the most significant contributors to crash risk. In this study, Dynamic Bayesian Network (DBN) was the framework of the RTCPM. Random Forest (RF) method was employed to identify the most important variables, which were used to build DBN-based RTCPMs. The PC algorithm combined with expert experience was further applied to investigate the coupling interdependency among traffic flow characteristics in the DBN model. A comparative analysis among the improved DBN-based RTCPM considering the interdependency, the original DBN-based RTCPM without considering the interdependency, and Multilayer Perceptron (MLP) was conducted. Besides, the sensitivity and strength of influences analyses were utilized to identify the most probable risk propagation path and the most significant contributors to crash risk. The results showed that the improved DBN-based RTCPM had better prediction performance than the original DBN-based RTCPM and the MLP based RTCPM. The most probable risk influencing path was identified as follows: speed on current segment (V) (time slice 2)⟶V (time slice 1)⟶speed on upstream segment (U_V) (time slice 1)⟶Traffic Performance Index (TPI) (time slice 1)⟶crash risk on current segment. The most sensitive contributor to crash risk in this path was V (time slice 2), followed by TPI (time slice 1), V (time slice 1), and U_V (time slice 1). These results indicate that the improved DBN-based RTCPM has the potential to predict crashes in real time for urban elevated expressway. Besides, it contributes to revealing the underlying mechanism of crash and formulating the real-time risk control measures.

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Singh, Sushil Kumar, Mikail Mohammed Salim, Jeonghun Cha, Yi Pan, and Jong Hyuk Park. "Machine Learning-Based Network Sub-Slicing Framework in a Sustainable 5G Environment." Sustainability 12, no. 15 (August 3, 2020): 6250. http://dx.doi.org/10.3390/su12156250.

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Nowadays, 5G network infrastructures are being developed for various industrial IoT (Internet of Things) applications worldwide, emerging with the IoT. As such, it is possible to deploy power-optimized technology in a way that promotes the long-term sustainability of networks. Network slicing is a fundamental technology that is implemented to handle load balancing issues within a multi-tenant network system. Separate network slices are formed to process applications having different requirements, such as low latency, high reliability, and high spectral efficiency. Modern IoT applications have dynamic needs, and various systems prioritize assorted types of network resources accordingly. In this paper, we present a new framework for the optimum performance of device applications with optimized network slice resources. Specifically, we propose a Machine Learning-based Network Sub-slicing Framework in a Sustainable 5G Environment in order to optimize network load balancing problems, where each logical slice is divided into a virtualized sub-slice of resources. Each sub-slice provides the application system with different prioritized resources as necessary. One sub-slice focuses on spectral efficiency, whereas the other focuses on providing low latency with reduced power consumption. We identify different connected device application requirements through feature selection using the Support Vector Machine (SVM) algorithm. The K-means algorithm is used to create clusters of sub-slices for the similar grouping of types of application services such as application-based, platform-based, and infrastructure-based services. Latency, load balancing, heterogeneity, and power efficiency are the four primary key considerations for the proposed framework. We evaluate and present a comparative analysis of the proposed framework, which outperforms existing studies based on experimental evaluation.

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Abbas, Khizar, Muhammad Afaq, Talha Ahmed Khan, Adeel Rafiq, and Wang-Cheol Song. "Slicing the Core Network and Radio Access Network Domains through Intent-Based Networking for 5G Networks." Electronics 9, no. 10 (October 18, 2020): 1710. http://dx.doi.org/10.3390/electronics9101710.

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The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency.

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Kułacz, Łukasz, Paweł Kryszkiewicz, and Adrian Kliks. "Waveform Flexibility for Network Slicing." Wireless Communications and Mobile Computing 2019 (March 27, 2019): 1–15. http://dx.doi.org/10.1155/2019/6250804.

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We discuss the idea of waveform flexibility and resource allocation in future wireless networks as a promising tool for network slicing implementation down to the lowest layers of the OSI (Open Systems Interconnection) models. In particular, we consider the possibility of cognitively adjusting the shape of the waveform to the requirements associated with various network slices. Moreover, such an adjustment of waveform shape is realised jointly with the selection and allocation of the appropriate frequency bands to each slice. In our approach, the definition of the waveform, as well as the assignment of resources, is done based on the information about the surrounding environment and each slice requirement stored in a dedicated context-information database. In this paper, we present the key concept of waveform flexibility for network slicing, the proposed algorithm for waveform selection and resource allocation among slices, and the achieved simulation results.

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Sulaymanova, Rimma T., Radik M. Khayrullin, Anna I. Lebedeva, Luisa I. Sulaymanova, and Eliza D. Askhabova. "Maternal body estrogen exposure influences the mice offspring ovaries’ morphology." Pediatrician (St. Petersburg) 12, no. 6 (April 18, 2022): 55–62. http://dx.doi.org/10.17816/ped12655-62.

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Background. The question of the effect of female sex hormones and their analogues on humans and experimental animals is of great interest in medicine. Aim. The aim of the work was to study the morphological features of the ovaries of the offspring of laboratory mice during the administration of estrogens to the maternal body. Materials and methods. Female laboratory mice after fertilization were divided into groups: two control and two experimental, which at the stage of development of gestation E11.5 underwent intramuscular, single administration of experimental doses of estrogens. The first experimental group was injected with the synthetic drug synestrol in the form of a 2% oil solution at a total dose of 50 mcg / kg (n = 5; S-50), the first control group was injected with olive oil at a dose of 0.2 m/kg (n = 5). The second experimental group was injected with a 0.4 ml 0.0005% fulvestrant oil solution at a dose of 100 mcg/kg (n = 5; F-100), the second control group (n = 5) received sterile castor oil at a dose of 0.8 m/kg. Results. Persistent morphological changes are observed in the ovaries of the offspring of the first experimental group S-50: an increase in the average area of the cortical substance, a decrease in the area of the medulla, an increase in the average number of yellow bodies, an increase in the average number of luteal cells in the yellow body, a decrease in the total number of follicles and atretic bodies, indicating a violation of the folliculogenesis process, an increase in the average diameter of blood vessels demonstrating increased blood circulation. With the introduction of the drug fulvestrant 100 mcg / kg in the second experimental group F-100, morphological changes in the form of an increase in the average area of the cortical substance, a decrease in the average area of the medulla, sclerosis of the stromal component, accompanied by a restructuring of the vascular network with signs of atresia and cystic degeneration of the follicular epithelium in secondary and tertiary follicles are considered on a slice of the ovaries of the offspring. Conclusions. The obtained results of the study confirm the urgency of the problem of implementing complex measures aimed at limiting the effects of estrogenetic drugs introduced into the maternal body during pregnancy, in order to prevent adverse effects on the development of the ovaries of offspring.

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Wichary, Tomasz, Jordi Mongay Batalla, Constandinos X. Mavromoustakis, Jerzy Żurek, and George Mastorakis. "Network Slicing Security Controls and Assurance for Verticals." Electronics 11, no. 2 (January 11, 2022): 222. http://dx.doi.org/10.3390/electronics11020222.

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This paper focuses on the security challenges of network slice implementation in 5G networks. We propose that network slice controllers support security by enabling security controls at different network layers. The slice controller orchestrates multilevel domains with resources at a very high level but needs to understand how to define the resources at lower levels. In this context, the main outstanding security challenge is the compromise of several resources in the presence of an attack due to weak resource isolation at different levels. We analysed the current standards and trends directed to mitigate the vulnerabilities mentioned above, and we propose security controls and classify them by efficiency and applicability (easiness to develop). Security controls are a common way to secure networks, but they enforce security policies only in respective areas. Therefore, the security domains allow for structuring the orchestration principles by considering the necessary security controls to be applied. This approach is common for both vendor-neutral and vendor-dependent security solutions. In our classification, we considered the controls in the following fields: (i) fair resource allocation with dynamic security assurance, (ii) isolation in a multilayer architecture and (iii) response to DDoS attacks without service and security degradation.

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Mebarkia, Khalil, and Zoltán Zsóka. "QoS Impacts of Slice Traffic Limitation." Infocommunications journal 13, no. 3 (2021): 24–32. http://dx.doi.org/10.36244/icj.2021.3.3.

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Slicing is an essential building block of 5G networks and beyond. Different slices mean sets of traffic demands with different requirements, which need to be served over separated or shared network resources. Various service chaining methods applied to support slicing lead to different network load patterns, impacting the QoS experienced by the traffic. In this paper, we analyze QoS properties applying a theoretical model. We also suggest appropriate parameter setting policies in slice-aware service function chaining (SFC) algorithms to increase QoS. We evaluate several metrics in different analysis scenarios to show the advantages of the slice-aware approach.

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Kim, Yohan, Sunyong Kim, and Hyuk Lim. "Reinforcement Learning Based Resource Management for Network Slicing." Applied Sciences 9, no. 11 (June 9, 2019): 2361. http://dx.doi.org/10.3390/app9112361.

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Network slicing to create multiple virtual networks, called network slice, is a promising technology to enable networking resource sharing among multiple tenants for the 5th generation (5G) networks. By offering a network slice to slice tenants, network slicing supports parallel services to meet the service level agreement (SLA). In legacy networks, every tenant pays a fixed and roughly estimated monthly or annual fee for shared resources according to a contract signed with a provider. However, such a fixed resource allocation mechanism may result in low resource utilization or violation of user quality of service (QoS) due to fluctuations in the network demand. To address this issue, we introduce a resource management system for network slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning approach from each tenant’s point of view. First, the resource management for network slicing is modeled as a Markov Decision Process (MDP) with the state space, action space, and reward function. Then, we propose a Q-learning-based dynamic resource adjustment algorithm that aims at maximizing the profit of tenants while ensuring the QoS requirements of end-users. The numerical simulation results demonstrate that the proposed algorithm can significantly increase the profit of tenants compared to existing fixed resource allocation methods while satisfying the QoS requirements of end-users.

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Ampririt, Phudit, Ermioni Qafzezi, Kevin Bylykbashi, Makoto Ikeda, Keita Matsuo, and Leonard Barolli. "Application of Fuzzy Logic for Slice QoS in 5G Networks." International Journal of Mobile Computing and Multimedia Communications 12, no. 2 (April 2021): 18–35. http://dx.doi.org/10.4018/ijmcmc.2021040102.

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The fifth generation (5G) network is expected to be flexible to satisfy quality of service (QoS) requirements, and the software-defined network (SDN) with network slicing will be a good approach for admission control. In this paper, the authors present and compare two fuzzy-based schemes to evaluate the QoS (FSQoS). They call these schemes FSQoS1 and FSQoS2. The FSQoS1 considers three parameters: slice throughput (ST), slice delay (SD), and slice loss (SL). In FSQoS2, they consider as an additional parameter the slice reliability (SR). So, FSQoS2 has four input parameters. They carried out simulations for evaluating the performance of the proposed schemes. From simulation results, they conclude that the considered parameters have different effects on the QoS performance. The FSQoS2 is more complex than FSQoS1, but it has a better performance for evaluating QoS. When ST and SR are increasing, the QoS parameter is increased. But, when SD and SL are increasing, the QoS is decreased. When ST is 0.1, SD is 0.1, SL is 0.1, and the QoS is increased by 32.02% when SR is increased from 0.3 to 0.8.

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Kaur, Amandeep, Ajay Pal Singh Chauhan, and Ashwani Kumar Aggarwal. "An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network." Expert Systems with Applications 186 (December 2021): 115686. http://dx.doi.org/10.1016/j.eswa.2021.115686.

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Sciancalepore, Vincenzo, Xavier Costa-Perez, and Albert Banchs. "RL-NSB: Reinforcement Learning-Based 5G Network Slice Broker." IEEE/ACM Transactions on Networking 27, no. 4 (August 2019): 1543–57. http://dx.doi.org/10.1109/tnet.2019.2924471.

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Nour, Boubakr, Adlen Ksentini, Nicolas Herbaut, Pantelis A. Frangoudis, and Hassine Moungla. "A Blockchain-Based Network Slice Broker for 5G Services." IEEE Networking Letters 1, no. 3 (September 2019): 99–102. http://dx.doi.org/10.1109/lnet.2019.2915117.

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Challa, Rajesh, Vyacheslav V. Zalyubovskiy, Syed M. Raza, Hyunseung Choo, and Aloknath De. "Network Slice Admission Model: Tradeoff Between Monetization and Rejections." IEEE Systems Journal 14, no. 1 (March 2020): 657–60. http://dx.doi.org/10.1109/jsyst.2019.2904667.

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Meneses, Flávio, Rui Silva, Daniel Corujo, Augusto Neto, and Rui L. Aguiar. "Dynamic network slice resources reconfiguration in heterogeneous mobility environments." Internet Technology Letters 2, no. 4 (May 29, 2019): e107. http://dx.doi.org/10.1002/itl2.107.

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Hewa, Tharaka, Pawani Porambage, Anshuman Kalla, Diana Pamela Moya Osorio, Madhusanka Liyanage, and Mika Ylianttila. "Blockchain and Game Theory Convergence for Network Slice Brokering." Computer 56, no. 3 (March 2023): 80–91. http://dx.doi.org/10.1109/mc.2022.3165533.

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Cui, Yaping, Xinyun Huang, Dapeng Wu, and Hao Zheng. "Machine Learning-Based Resource Allocation Strategy for Network Slicing in Vehicular Networks." Wireless Communications and Mobile Computing 2020 (November 17, 2020): 1–10. http://dx.doi.org/10.1155/2020/8836315.

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The diversified service requirements in vehicular networks have stimulated the investigation to develop suitable technologies to satisfy the demands of vehicles. In this context, network slicing has been considered as one of the most promising architectural techniques to cater to the various strict service requirements. However, the unpredictability of the service traffic of each slice caused by the complex communication environments leads to a weak utilization of the allocated slicing resources. Thus, in this paper, we use Long Short-Term Memory- (LSTM-) based resource allocation to reduce the total system delay. Specially, we first formulated the radio resource allocation problem as a convex optimization problem to minimize system delay. Secondly, to further reduce delay, we design a Convolutional LSTM- (ConvLSTM-) based traffic prediction to predict traffic of complex slice services in vehicular networks, which is used in the resource allocation processing. And three types of traffic are considered, that is, SMS, phone, and web traffic. Finally, based on the predicted results, i.e., the traffic of each slice and user load distribution, we exploit the primal-dual interior-point method to explore the optimal slice weight of resources. Numerical results show that the average error rates of predicted SMS, phone, and web traffic are 25.0%, 12.4%, and 12.2%, respectively, and the total delay is significantly reduced, which verifies the accuracy of the traffic prediction and the effectiveness of the proposed strategy.

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Zou, Hong, Min Zhou, Yaping Cui, Peng He, Hong Zhang, and Ruyan Wang. "Service Provisioning in Sliced Cloud Radio Access Networks." Wireless Communications and Mobile Computing 2022 (February 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/7326172.

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Network slicing- (NS-) based cloud radio access networks (C-RANs) have emerged as a key paradigm to support various novel applications in 5G and beyond networks. However, it is still a challenge to allocate resources efficiently due to heterogeneous quality of service (QoS) requirements of diverse services as well as competition among different network slices. In this paper, we consider a service provisioning allocation framework to guarantee resource utilization while ensuring the QoS of users. Specifically, an inter/intraslice bandwidth optimization strategy is developed to maximize the revenue of the system with multiple network slices. The proposed strategy is hierarchically structured, which decomposes into network-level slicing and packet scheduling level slicing. At the network level, resources are allocated to each slice. At the packet scheduling level, each slice allocates physical resource blocks (PRBs) among users associated with the slice. Numerical results show that the proposed strategy can effectively improve the revenue of the system while guaranteeing heterogeneous QoS requirements. For example, the revenue of the proposed strategy is 21% higher than that of the average allocation strategy.

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Al-Yassari, Mohammed Mousa Rashid, and Nadia Adnan Shiltagh Al-Jamali. "Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment." Journal of Engineering 29, no. 6 (June 1, 2023): 87–97. http://dx.doi.org/10.31026/j.eng.2023.06.07.

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The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%.

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Wu, Guomin, Guoping Tan, and Defu Jiang. "A Distributed E-Cross Learning Algorithm for Intelligent Multiple Network Slice Selection." Wireless Communications and Mobile Computing 2021 (May 16, 2021): 1–14. http://dx.doi.org/10.1155/2021/8875515.

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Recently, some technological issues in network slicing have been explored. However, most works focus on the physical resource management in this research field and less on slice selection. Different from the existing studies, we explore the problem of intelligent multiple slice selection, which makes some effort to dynamically obtain better user experience in a changeable state. Herein, we consider two factors about user experience: its throughput and energy consumption. Accordingly, a distributed E-cross learning algorithm is developed in the multiagent system where each terminal is regarded as an agent in the distributed network. Furthermore, its convergence is theoretically proven for the dynamic game model. In addition, the complexity of the proposed algorithm is discussed. A mass of simulation results are presented for the convergence and effectiveness of the proposed distributed learning algorithm. Compared with greedy algorithm, the proposed intelligent algorithm has a faster convergence speed. Besides, better user experience is attained effectively with multiple slice access.

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Wu, Chiping, Wah Ping Luk, Jesse Gillis, Frances Skinner, and Liang Zhang. "Size Does Matter: Generation of Intrinsic Network Rhythms in Thick Mouse Hippocampal Slices." Journal of Neurophysiology 93, no. 4 (April 2005): 2302–17. http://dx.doi.org/10.1152/jn.00806.2004.

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Rodent hippocampal slices of ≤0.5 mm thickness have been widely used as a convenient in vitro model since the 1970s. However, spontaneous population rhythmic activities do not consistently occur in this preparation due to limited network connectivity. To overcome this limitation, we develop a novel slice preparation of 1 mm thickness from adult mouse hippocampus by separating dentate gyrus from CA3/CA1 areas but preserving dentate–CA3-CA1 connectivity. While superfused in vitro at 32 or 37°C, the thick slice exhibits robust spontaneous network rhythms of 1–4 Hz that originate from the CA3 area. Via assessing tissue O2, K+, pH, synaptic, and single-cell activities of superfused thick slices, we verify that these spontaneous rhythms are not a consequence of hypoxia and nonspecific experimental artifacts. We suggest that the thick slice contains a unitary circuitry sufficient to generate intrinsic hippocampal network rhythms and this preparation is suitable for exploring the fundamental properties and plasticity of a functionally defined hippocampal “lamella” in vitro.

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Ho, Namgyu, and Yoon-Chul Kim. "Estimation of Cardiac Short Axis Slice Levels with a Cascaded Deep Convolutional and Recurrent Neural Network Model." Tomography 8, no. 6 (November 14, 2022): 2749–60. http://dx.doi.org/10.3390/tomography8060229.

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Automatic identification of short axis slice levels in cardiac magnetic resonance imaging (MRI) is important in efficient and precise diagnosis of cardiac disease based on the geometry of the left ventricle. We developed a combined model of convolutional neural network (CNN) and recurrent neural network (RNN) that takes a series of short axis slices as input and predicts a series of slice levels as output. Each slice image was labeled as one of the following five classes: out-of-apical, apical, mid, basal, and out-of-basal levels. A variety of multi-class classification models were evaluated. When compared with the CNN-alone models, the cascaded CNN-RNN models resulted in higher mean F1-score and accuracy. In our implementation and testing of four different baseline networks with different combinations of RNN modules, MobileNet as the feature extractor cascaded with a two-layer long short-term memory (LSTM) network produced the highest scores in four of the seven evaluation metrics, i.e., five F1-scores, area under the curve (AUC), and accuracy. Our study indicates that the cascaded CNN-RNN models are superior to the CNN-alone models for the classification of short axis slice levels in cardiac cine MR images.

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Nazir, Sajid, Vladimir Stanković, Ivan Andonović, and Dejan Vukobratović. "Application Layer Systematic Network Coding for Sliced H.264/AVC Video Streaming." Advances in Multimedia 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/916715.

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Application Layer Forward Error Correction (AL-FEC) with rateless codes can be applied to protect the video data over lossy channels. Expanding Window Random Linear Codes (EW RLCs) are a flexible unequal error protection fountain coding scheme which can provide prioritized data transmission. In this paper, we propose a system that exploits systematic EW RLC for H.264/Advanced Video Coding (AVC) slice-partitioned data. The system prioritizes slices based on their PSNR contribution to reconstruction as well as temporal significance. Simulation results demonstrate usefulness of using relative slice priority with systematic codes for multimedia broadcast applications.

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Guijarro, Luis, Jose Vidal, and Vicent Pla. "Competition in Service Provision between Slice Operators in 5G Networks." Electronics 7, no. 11 (November 12, 2018): 315. http://dx.doi.org/10.3390/electronics7110315.

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Network slicing is gaining an increasing importance as an effective way to introduce flexibility in the management of resources in 5G networks. We envision a scenario where a set of network operators outsource their respective networks to one Infrastructure Provider (InP), and use network slicing mechanisms to request the resources as needed for service provision. The InP is then responsible for the network operation and maintenance, while the network operators become Virtual Network Operators (VNOs). We model a setting where two VNOs compete for the users in terms of quality of service, by strategically distributing its share of the aggregated cells capacity managed by the InP among its subscribers. The results show that the rate is allocated among the subscribers at each cell in a way that mimics the overall share that each VNO is entitled to, and that this allocation is the Nash equilibrium of the strategic slicing game between the VNOs. We conclude that network sharing and slicing provide an attractive flexibility in the allocation of resources without the need to enforce a policy through the InP.

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Phillips, Wiktor S., Mikkel Herly, Christopher A. Del Negro, and Jens C. Rekling. "Organotypic slice cultures containing the preBötzinger complex generate respiratory-like rhythms." Journal of Neurophysiology 115, no. 2 (February 1, 2016): 1063–70. http://dx.doi.org/10.1152/jn.00904.2015.

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Study of acute brain stem slice preparations in vitro has advanced our understanding of the cellular and synaptic mechanisms of respiratory rhythm generation, but their inherent limitations preclude long-term manipulation and recording experiments. In the current study, we have developed an organotypic slice culture preparation containing the preBötzinger complex (preBötC), the core inspiratory rhythm generator of the ventrolateral brain stem. We measured bilateral synchronous network oscillations, using calcium-sensitive fluorescent dyes, in both ventrolateral (presumably the preBötC) and dorsomedial regions of slice cultures at 7–43 days in vitro. These calcium oscillations appear to be driven by periodic bursts of inspiratory neuronal activity, because whole cell recordings from ventrolateral neurons in culture revealed inspiratory-like drive potentials, and no oscillatory activity was detected from glial fibrillary associated protein-expressing astrocytes in cultures. Acute slices showed a burst frequency of 10.9 ± 4.2 bursts/min, which was not different from that of brain stem slice cultures (13.7 ± 10.6 bursts/min). However, slice cocultures that include two cerebellar explants placed along the dorsolateral border of the brainstem displayed up to 193% faster burst frequency (22.4 ± 8.3 bursts/min) and higher signal amplitude (340%) compared with acute slices. We conclude that preBötC-containing slice cultures retain inspiratory-like rhythmic function and therefore may facilitate lines of experimentation that involve extended incubation (e.g., genetic transfection or chronic drug exposure) while simultaneously being amenable to imaging and electrophysiology at cellular, synaptic, and network levels.

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Jain, Hemant, Vinay Chamola, Yash Jain, and Naren. "5G network slice for digital real-time healthcare system powered by network data analytics." Internet of Things and Cyber-Physical Systems 1 (2021): 14–21. http://dx.doi.org/10.1016/j.iotcps.2021.12.001.

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Alotaibi, Daifallah. "Survey on Network Slice Isolation in 5G Networks: Fundamental Challenges." Procedia Computer Science 182 (2021): 38–45. http://dx.doi.org/10.1016/j.procs.2021.02.006.

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Guan, Wanqing, and Haijun Zhang. "Demand prediction based slice reconfiguration using dueling deep Q-network." China Communications 19, no. 5 (May 2022): 267–85. http://dx.doi.org/10.23919/jcc.2022.05.004.

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Li, Chaoran, and Yun Liu. "ESMART: Energy-Efficient Slice-Mix-Aggregate for Wireless Sensor Network." International Journal of Distributed Sensor Networks 9, no. 12 (January 2013): 134509. http://dx.doi.org/10.1155/2013/134509.

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Kozicki, Bartłomiej, Hidehiko Takara, Yukio Tsukishima, Toshihide Yoshimatsu, Kazushige Yonenaga, and Masahiko Jinno. "Experimental demonstration of spectrum-sliced elastic optical path network (SLICE)." Optics Express 18, no. 21 (October 4, 2010): 22105. http://dx.doi.org/10.1364/oe.18.022105.

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Journal articles on the topic 'Gestion de Network Slice'

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