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Добірка наукової літератури з теми "VNF Scaling"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "VNF Scaling"

Zafar, Saima, Usman Ayub, Hend I. Alkhammash, and Nasim Ullah. "Framework for Efficient Auto-Scaling of Virtual Network Functions in a Cloud Environment." Sensors 22, no. 19 (October 7, 2022): 7597. http://dx.doi.org/10.3390/s22197597.

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Анотація:

Network Function Virtualization (NFV) offers an alternate method to design, deploy and manage network services. The NFV decouples network functions from the dedicated hardware and moves them to the virtual servers so that they can run in the software. One of the major strengths of the NFV is its ability to dynamically extend or reduce resources allocated to Virtual Network Functions (VNF) as needed and at run-time. There is a need for a comprehensive metering component in the cloud to store and process the metrics/samples for efficient auto-scaling or load-management of the VNF. In this paper, we propose an integrating framework for efficient auto-scaling of VNF using Gnocchi; a time-series database that is integrated within the framework to store, handle and index the time-series data. The objective of this study is to validate the efficacy of employing Gnocchi for auto-scaling of VNF, in terms of aggregated data points, database size, data recovery speed, and memory consumption. The employed methodology is to perform a detailed empirical analysis of the proposed framework by deploying a fully functional cloud to implement NFV architecture using several OpenStack components including Gnocchi. Our results show a significant improvement over the legacy Ceilometer configuration in terms of lower metering storage size, less memory utilization in processing and management of metrics, and reduced time delay in retrieving the monitoring data to evaluate alarms for the auto-scaling of VNF.

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Hu, Haiyan, Qiaoyan Kang, Shuo Zhao, Jianfeng Wang, and Youbin Fu. "Service Function Chain Deployment Method Based on Traffic Prediction and Adaptive Virtual Network Function Scaling." Electronics 11, no. 16 (August 22, 2022): 2625. http://dx.doi.org/10.3390/electronics11162625.

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With the development of network function virtualization (NFV), the resource management of service function chains (SFC) in the virtualized environment has gradually become a research hotspot. Usually, users hope that they can get the network services they want anytime and anywhere. The network service requests are dynamic and real-time, which requires that the SFC in the NFV environment can also meet the dynamically changing network service requests. In this regard, this paper proposes an SFC deployment method based on traffic prediction and adaptive virtual network function (VNF) scaling. Firstly, an improved network traffic prediction method is proposed to improve its prediction accuracy for dynamically changing network traffic. Secondly, the predicted traffic data is processed for the subsequent scaling of the VNF. Finally, an adaptive VNF scaling method is designed for the purpose of dynamic management of network virtual resources. The experimental results show that the method proposed in this paper can manage the network resources in dynamic scenarios. It can effectively improve the availability of network services, reduce the operating overhead and achieve a good optimization effect.

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Naidu, D. J. Samatha, and G. Hima Bindu. "INVESTIGATION ON ONLINE VNF SCALING IN A CLOUD DATACENTER USING ILP." International Journal of Computer Science and Mobile Computing 10, no. 8 (August 30, 2021): 32–35. http://dx.doi.org/10.47760/ijcsmc.2021.v10i08.005.

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NFV is the advanced technology in present situation. Online VNF Scaling in a cloud datacenter under multi-resource constraints were consider for formulating mathematical model. A new novel ILP Scaling algorithm works based on the regularization technique and dependent rounding.

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Yao, Yifu, Songtao Guo, Pan Li, Guiyan Liu, and Yue Zeng. "Forecasting assisted VNF scaling in NFV-enabled networks." Computer Networks 168 (February 2020): 107040. http://dx.doi.org/10.1016/j.comnet.2019.107040.

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Zeng, Zhihao, Zixiang Xia, Xiaoning Zhang, and Yexiao He. "SFC Design and VNF Placement Based on Traffic Volume Scaling and VNF Dependency in 5G Networks." Computer Modeling in Engineering & Sciences 134, no. 3 (2023): 1791–814. http://dx.doi.org/10.32604/cmes.2022.021648.

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Luo, Ziyue, and Chuan Wu. "An Online Algorithm for VNF Service Chain Scaling in Datacenters." IEEE/ACM Transactions on Networking 28, no. 3 (June 2020): 1061–73. http://dx.doi.org/10.1109/tnet.2020.2979263.

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Llorens-Carrodeguas, Alejandro, Irian Leyva-Pupo, Cristina Cervelló-Pastor, Luis Piñeiro, and Shuaib Siddiqui. "An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters." Sensors 21, no. 24 (December 11, 2021): 8283. http://dx.doi.org/10.3390/s21248283.

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Анотація:

This paper studies the problem of the dynamic scaling and load balancing of transparent virtualized network functions (VNFs). It analyzes different particularities of this problem, such as loop avoidance when performing scaling-out actions, and bidirectional flow affinity. To address this problem, a software-defined networking (SDN)-based solution is implemented consisting of two SDN controllers and two OpenFlow switches (OFSs). In this approach, the SDN controllers run the solution logic (i.e., monitoring, scaling, and load-balancing modules). According to the SDN controllers instructions, the OFSs are responsible for redirecting traffic to and from the VNF clusters (i.e., load-balancing strategy). Several experiments were conducted to validate the feasibility of this proposed solution on a real testbed. Through connectivity tests, not only could end-to-end (E2E) traffic be successfully achieved through the VNF cluster, but the bidirectional flow affinity strategy was also found to perform well because it could simultaneously create flow rules in both switches. Moreover, the selected CPU-based load-balancing method guaranteed an average imbalance below 10% while ensuring that new incoming traffic was redirected to the least loaded instance without requiring packet modification. Additionally, the designed monitoring function was able to detect failures in the set of active members in near real-time and active new instances in less than a minute. Likewise, the proposed auto-scaling module had a quick response to traffic changes. Our solution showed that the use of SDN controllers along with OFS provides great flexibility to implement different load-balancing, scaling, and monitoring strategies.

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Wu, Ziyan, Tianming Cui, Arvind Narayanan, Yang Zhang, Kangjie Lu, Antonia Zhai, and Zhi-Li Zhang. "GranularNF." ACM SIGMETRICS Performance Evaluation Review 50, no. 2 (August 30, 2022): 46–51. http://dx.doi.org/10.1145/3561074.3561092.

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Анотація:

In this paper, we consider the challenges that arise from the need to scale virtualized network functions (VNFs) at 100 Gbps line speed and beyond. Traditional VNF designs are monolithic in state management and scheduling: internally maintaining all states and operations associated with them. Without proper design considerations, it suffers from limitations when scaling at 100 Gbps link speed and beyond: the inability of efficient utilization of the cache because of the contention due to the frequent control plane activities, computational/memory-intensive tasks taking up CPU times, shares states causing the synchronization among the cores. We address these limitations by arguing for the need to granularly decompose a VNF into data/control components that are co-located within a server but can be independently scaled among the cores. To realize the approach, we design a "serverless" programming framework with novel abstraction to optimize the data components that must process packets at the line speed, reduce the contention of the data states and enable run-time scheduling of different components for improved resource utilization. The abstractions, combined with the runtime system that we design, help NFV developers focus on the logic and correctness of VNF programming without worrying about how VNFs may be scaled in or out. We evaluate our platform by comparing it with monolithic approaches using different workloads and by analyzing its advantages of separation on scalability, performance determinism, and feature velocity.

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Tang, Hong, Danny Zhou, and Duan Chen. "Dynamic Network Function Instance Scaling Based on Traffic Forecasting and VNF Placement in Operator Data Centers." IEEE Transactions on Parallel and Distributed Systems 30, no. 3 (March 1, 2019): 530–43. http://dx.doi.org/10.1109/tpds.2018.2867587.

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Xu, Ran. "Proactive VNF Scaling with Heterogeneous Cloud Resources: Fusing Long Short-Term Memory Prediction and Cooperative Allocation." Mathematical Problems in Engineering 2020 (January 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/4371056.

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Анотація:

Network function virtualization (NFV) is designed to implement network functions by software that replaces proprietary hardware devices in traditional networks. In response to the growing demand of resource-intensive services, for NFV cloud service providers, software-oriented network functions face a number of challenges, such as dynamic deployment of virtual network functions and efficient allocation of multiple resources. This study aims at the dynamic allocation and adjustment of network multiresources and multitype flows for NFV. First, to seek a proactive approach to provision new instances for overloaded VNFs ahead of time, a model called long short-term memory recurrent neural network (LSTM RNN) is proposed to estimate flows in this paper. Then, based on the estimated flow, a cooperative and complementary resource allocation algorithm is designed to reduce resource fragmentation and improve the utilization. The final results demonstrate the advantage of the LSTM model on predicting the network function flow requirements, and our algorithm achieves good results and performance improvement in dynamically expanding network functions and improving resource utilization.

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Більше джерел

Дисертації з теми "VNF Scaling"

Subramanya, Tejas. "Autonomic Management and Orchestration Strategies in MEC-Enabled 5G Networks." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320883.

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Анотація:

5G and beyond mobile network technology promises to deliver unprecedented ultra-low latency and high data rates, paving the way for many novel applications and services. Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) are two technologies expected to play a vital role in achieving ambitious Quality of Service requirements of such applications. While NFV provides flexibility by enabling network functions to be dynamically deployed and inter-connected to realize Service Function Chains (SFC), MEC brings the computing capability to the mobile network's edges, thus reducing latency and alleviating the transport network load. However, adequate mechanisms are needed to meet the dynamically changing network service demands (i.e., in single and multiple domains) and optimally utilize the network resources while ensuring that the end-to-end latency requirement of services is always satisfied. In this dissertation work, we break the problem into three separate stages and present the solutions for each one of them.Firstly, we apply Artificial Intelligence (AI) techniques to drive NFV resource orchestration in MEC-enabled 5G architectures for single and multi-domain scenarios. We propose three deep learning approaches to perform horizontal and vertical Virtual Network Function (VNF) auto-scaling: (i) Multilayer Perceptron (MLP) classification and regression (single-domain), (ii) Centralized Artificial Neural Network (ANN), centralized Long-Short Term Memory (LSTM) and centralized Convolutional Neural Network-LSTM (CNN-LSTM) (single-domain), and (iii) Federated ANN, federated LSTM and federated CNN-LSTM (multi-domain). We evaluate the performance of each of these deep learning models trained over a commercial network operator dataset and investigate the pros and cons of different approaches for VNF auto-scaling. For the first approach, our results show that both MLP classifier and MLP regressor models have strong predicting capability for auto-scaling. However, MLP regressor outperforms MLP classifier in terms of accuracy. For the second approach (one-step prediction), CNN-LSTM performs the best for the QoS-prioritized objective and LSTM performs the best for the cost-prioritized objective. For the second approach (multi-step prediction), the encoder-decoder CNN-LSTM model outperforms the encoder-decoder LSTM model for both QoS and Cost prioritized objectives. For the third approach, both federated LSTM and federated CNN-LSTM models perform equally better than the federated ANN model. It was also noted that in general federated learning approaches performs poorly compared to centralized learning approaches. Secondly, we employ Integer Linear Programming (ILP) techniques to formulate and solve a joint user association and SFC placement problem, where each SFC represents a service requested by a user with end-to-end latency and data rate requirements. We also develop a comprehensive end-to-end latency model considering radio delay, backhaul network delay and SFC processing delay for 5G mobile networks. We evaluated the proposed model using simulations based on real-operator network topology and real-world latency values. Our results show that the average end-to-end latency reduces significantly when SFCs are placed at the ME hosts according to their latency and data rate demands. Furthermore, we propose an heuristic algorithm to address the issue of scalability in ILP, that can solve the above association/mapping problem in seconds rather than hours.Finally, we introduce lightMEC - a lightweight MEC platform for deploying mobile edge computing functionalities which allows hosting of low-latency and bandwidth-intensive applications at the network edge. Measurements conducted over a real-life test demonstrated that lightMEC could actually support practical MEC applications without requiring any change to existing mobile network nodes' functionality in the access and core network segments. The significant benefits of adopting the proposed architecture are analyzed based on a proof-of-concept demonstration of the content caching use case. Furthermore, we introduce the AI-driven Kubernetes orchestration prototype that we implemented by leveraging the lightMEC platform and assess the performance of the proposed deep learning models (from stage 1) in an experimental setup. The prototype evaluations confirm the simulation results achieved in stage 1 of the thesis.

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Kishore, Aravind. "Laminar Plunging Jets - Interfacial Rupture and Inception of Entrainment." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1397476562.

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Частини книг з теми "VNF Scaling"

Ge, Hongwu, Yonghua Huo, Zhihao Wang, Ping Xie, and Tongyan Wei. "VNF Instance Dynamic Scaling Strategy Based on LSTM." In Advances in Intelligent Systems and Computing, 335–43. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8462-6_39.

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Zhuang, Weihua, and Kaige Qu. "Dynamic VNF Resource Scaling and Migration: A Machine Learning Approach." In Wireless Networks, 85–129. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87136-9_4.

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Тези доповідей конференцій з теми "VNF Scaling"

Wang, Xiaoke, Chuan Wu, Franck Le, Alex Liu, Zongpeng Li, and Francis Lau. "Online VNF Scaling in Datacenters." In 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. http://dx.doi.org/10.1109/cloud.2016.0028.

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Wang, Zenan, Jiao Zhang, Haoran Wei, and Tao Huang. "Hieff: Enabling Efficient VNF Clusters by Coordinating VNF Scaling and Flow Scheduling." In 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC). IEEE, 2020. http://dx.doi.org/10.1109/ipccc50635.2020.9391534.

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Ren, Yi, Tuan Phung-Duc, Yi-Kuan Liu, Jyh-Cheng Chen, and Yi-Hao Lin. "ASA: Adaptive VNF Scaling Algorithm for 5G Mobile Networks." In 2018 IEEE 7th International Conference on Cloud Networking (CloudNet). IEEE, 2018. http://dx.doi.org/10.1109/cloudnet.2018.8549542.

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Soto, Paola, Danny De Vleeschauwer, Miguel Camelo, Yorick De Bock, Koen De Schepper, Chia-Yu Chang, Peter Hellinckx, Juan F. Botero, and Steven Latre. "Towards Autonomous VNF Auto-scaling using Deep Reinforcement Learning." In 2021 Eighth International Conference on Software Defined Systems (SDS). IEEE, 2021. http://dx.doi.org/10.1109/sds54264.2021.9731854.

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Seo, Namjin, DongNyeong Heo, Jibum Hong, Hee-Gon Kim, Jae-Hyoung Yoo, James Won-Ki Hong, and Heevoul Choi. "Updating VNF deployment with Scaling Actions using Reinforcement Algorithms." In 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 2022. http://dx.doi.org/10.23919/apnoms56106.2022.9919943.

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Vu, Xuan Tuong, Jangwon Lee, Quang Huy Nguyen, Kyoungjae Sun, and Younghan Kim. "An architecture for enabling VNF auto-scaling with flow migration." In 2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2020. http://dx.doi.org/10.1109/ictc49870.2020.9289507.

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Wang, Xiaoke, Chuan Wu, Franck Le, and Francis C. M. Lau. "Online Learning-Assisted VNF Service Chain Scaling with Network Uncertainties." In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). IEEE, 2017. http://dx.doi.org/10.1109/cloud.2017.34.

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Tong, Riming, Siya Xu, Bo Hu, Jinghong Zhao, Lei Jin, Shaoyong Guo, and Wenjing Li. "VNF Dynamic Scaling and Deployment Algorithm Based on Traffic Prediction." In 2020 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2020. http://dx.doi.org/10.1109/iwcmc48107.2020.9148479.

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Fei, Xincai, Fangming Liu, Hong Xu, and Hai Jin. "Adaptive VNF Scaling and Flow Routing with Proactive Demand Prediction." In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications. IEEE, 2018. http://dx.doi.org/10.1109/infocom.2018.8486320.

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Tashtarian, Farzad, Amir Varasteh, Ahmadreza Montazerolghaem, and Wolfgang Kellerer. "Distributed VNF scaling in large-scale datacenters: An ADMM-based approach." In 2017 IEEE 17th International Conference on Communication Technology (ICCT). IEEE, 2017. http://dx.doi.org/10.1109/icct.2017.8359682.

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Звіти організацій з теми "VNF Scaling"

Staples, John. Frequency Scaling VHF Photoinjector Cavity. Office of Scientific and Technical Information (OSTI), April 2007. http://dx.doi.org/10.2172/1235574.

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