Relevant bibliographies by topics / VNF Scaling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'VNF Scaling'

Author: Grafiati
Published: 11 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "VNF Scaling"

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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 (2022): 7597. http://dx.doi.org/10.3390/s22197597.

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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,
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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 (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. Fir
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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 (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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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 (2021): 8283. http://dx.doi.org/10.3390/s21248283.

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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, th
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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 (2020): 1061–73. http://dx.doi.org/10.1109/tnet.2020.2979263.

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Sarrigiannis, Ioannis, Kostas Ramantas, Elli Kartsakli, Prodromos-Vasileios Mekikis, Angelos Antonopoulos, and Christos Verikoukis. "Online VNF Lifecycle Management in a MEC-enabled 5G IoT Architecture." IEEE Internet of Things Journal 7, no. 5 (2020): 4183–94. https://doi.org/10.1109/JIOT.2019.2944695.

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The upcoming fifth generation (5G) mobile communications urge software-defined networks (SDNs) and network function virtualization (NFV) to join forces with the multiaccess edge computing (MEC) cause. Thus, reduced latency and increased capacity at the edge of the network can be achieved, to satisfy the requirements of the Internet of Things (IoT) ecosystem. If not properly orchestrated, the flexibility of the virtual network functions (VNFs) incorporation, in terms of deployment and lifecycle management, may cause serious issues in the NFV scheme. As the service level agreements (SLAs) of the
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Wu, Ziyan, Tianming Cui, Arvind Narayanan, et al. "GranularNF." ACM SIGMETRICS Performance Evaluation Review 50, no. 2 (2022): 46–51. http://dx.doi.org/10.1145/3561074.3561092.

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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 sta
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Moreno-Vozmediano, Rafael, Eduardo Huedo, Rubén S. Montero, and Ignacio M. Llorente. "AI-Driven Resource Allocation and Auto-Scaling of VNFs in Edge-5G-IoT Ecosystems." Electronics 14, no. 9 (2025): 1808. https://doi.org/10.3390/electronics14091808.

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With the rapid expansion of edge-5G-IoT ecosystems, the need for intelligent and adaptive resource management strategies has become a critical challenge. In these environments, Virtualized Network Functions (VNFs) deployed at the network edge must handle highly dynamic workloads, making fixed resource allocation inefficient. While over-provisioning can lead to unnecessary resource waste, an especially critical issue in edge environments with limited resources, under-provisioning can degrade performance and service quality. This paper presents an AI-based predictive auto-scaling framework desig
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Dissertations / Theses on the topic "VNF Scaling"

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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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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 redu
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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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Book chapters on the topic "VNF Scaling"

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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. 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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87136-9_4.

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Seo, Namjin, DongNyeong Heo, and Heeyoul Choi. "Advanced VNF Scaling in Network Management with Reinforcement Learning and Graph Neural Networks." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6951-6_5.

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Zhou, Yunyi, Ruohan Gao, Xinping Zheng, Yuchen Huang, and Zhixuan Chu. "VMFTransformer: An Angle-Preserving and Auto-Scaling Machine for Multi-Horizon Probabilistic Forecasting." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240835.

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As deep learning develops, the major research methodologies of time series forecasting can be divided into two categories, i.e., iterative and direct methods. In the iterative methods, since a small amount of error is produced at each time step, the recursive structure can potentially lead to large error accumulations over longer forecasting horizons. Although the direct methods can avoid this puzzle involved in the iterative methods, they face abuse of conditional independence among time points. This impractical assumption can also lead to biased models. To solve these challenges, we propose
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Conference papers on the topic "VNF Scaling"

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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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Chai, Xuqing, Ying Wang, Manjun Zhang, and Lin Cong. "Efficient VNF-FG Scaling Algorithm for 5G Network Slices." In 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, 2023. http://dx.doi.org/10.1109/bmsb58369.2023.10211239.

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Soto, Paola, Danny De Vleeschauwer, Miguel Camelo, et al. "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, et al. "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, et al. "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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Reports on the topic "VNF Scaling"

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

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Journal articles
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