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Journal articles on the topic 'Service Function Chains'

Author: Grafiati

Published: 6 September 2023

Last updated: 25 July 2025

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1

Behrooz, Farkiani, Bakhsh Bahador, A. Mirhassan S., Wauter Tim, Volckaer Bruno, and De Turck Filip. "Prioritized Deployment of Dynamic Service Function Chains." Transactions on Networking 29, no. 3 (2021): 979–93. https://doi.org/10.5281/zenodo.6120040.

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Service Function Chaining and Network Function Virtualization are enabling technologies that provide dynamic network services with diverse QoS requirements. Regarding the limited infrastructure resources, service providers need to prioritize service requests and even reject some of low-priority requests to satisfy the requirements of high-priority services. In this paper, we study the problem of deployment and reconfiguration of a set of chains with different priorities with the objective of maximizing the service provider's profit; wherein, we also consider management concerns including t

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2

Tarik Moufakir, Mohamed Faten Zhani, Abdelouahed Gherbi, Moayad Aloqaily, and Nadir Ghrada. "SFCaaS: Service function chains as a service in NFV environments." ITU Journal on Future and Evolving Technologies 3, no. 3 (2022): 679–92. http://dx.doi.org/10.52953/zpdb8065.

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With the growing deployment of emergent technologies like software-defined networking, network services are expected to be revolutionized. In this paper, we investigate offering Service Function Chains as a Service (SFCaaS) in NFV environments. We describe the potential business model to offer such a service and then we address the service function chain provisioning and resource allocation problem. As the chain is deployed thanks to virtual machines (i.e., instances) and links, we conduct first a detailed study of the costs of Amazon EC2 instances with respect to their location, size, type an

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3

Nguyen, Trinh, and Myungsik Yoo. "Mechanism for Dynamically Updating Service Function Chains in NFV." Journal of Korean Institute of Communications and Information Sciences 43, no. 9 (2018): 1476–82. http://dx.doi.org/10.7840/kics.2018.43.9.1476.

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4

B.R.Vamsi, Krishna Naik*1 &. V. SaraswathiBai2. "OPTIMAL NETWORK SERVICE CHAIN PROVISION COMPUTING." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 11 (2018): 29–50. https://doi.org/10.5281/zenodo.1482356.

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Service chains consist of a set of network services, such as firewalls or application delivery controllers, which are interconnected through a network to support various applications. While it is not a new concept, there has been an extremely important new trend with the rise of software-defined network (SDN) and Network Function Virtualization (NFV). The combination of SDN and NFV can make the service chain and application provisioning process much shorter and simpler. In this paper, we study the provisioning of service chains jointly with the number/location of virtual network functions (VNF

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5

Khosravian, Pouya, Sima Emadi, Ghasem Mirjalily, and Behzad Zamani. "QoS-aware service composition based on context-free grammar and skyline in service function chaining using genetic algorithm." PeerJ Computer Science 7 (July 26, 2021): e603. http://dx.doi.org/10.7717/peerj-cs.603.

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Service function chaining (SFC) is a mechanism that allows service providers to combine various service functions and exploit the available virtual infrastructure. The best selection of virtual services in the network is essential for meeting user requirements and constraints. This paper proposes a novel approach to generate the optimal composition of the service functions. To this end, a genetic algorithm based on context-free grammar (CFG) that adheres to the Internet Engineering Task Force (IETF) standard and Skyline was developed to use in SFC. The IETF uses cases of the data center, secur

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Jalalitabar, Maryam, Evrim Guler, Danyang Zheng, Guangchun Luo, Ling Tian, and Xiaojun Cao. "Embedding Dependence-Aware Service Function Chains." Journal of Optical Communications and Networking 10, no. 8 (2018): C64. http://dx.doi.org/10.1364/jocn.10.000c64.

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7

Farkiani, Behrooz, Bahador Bakhshi, S. Ali MirHassani, Tim Wauters, Bruno Volckaert, and Filip De Turck. "Prioritized Deployment of Dynamic Service Function Chains." IEEE/ACM Transactions on Networking 29, no. 3 (2021): 979–93. http://dx.doi.org/10.1109/tnet.2021.3055074.

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8

Cai, Jun, Zhongwei Huang, Jianzhen Luo, Yan Liu, Huimin Zhao, and Liping Liao. "Composing and deploying parallelized service function chains." Journal of Network and Computer Applications 163 (August 2020): 102637. http://dx.doi.org/10.1016/j.jnca.2020.102637.

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9

Katsikas, Georgios P., Marcel Enguehard, Maciej Kuźniar, Gerald Q. Maguire Jr, and Dejan Kostić. "SNF: synthesizing high performance NFV service chains." PeerJ Computer Science 2 (November 14, 2016): e98. http://dx.doi.org/10.7717/peerj-cs.98.

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In this paper we introduce SNF, a framework that synthesizes (S) network function (NF) service chains by eliminating redundant I/O and repeated elements, while consolidating stateful cross layer packet operations across the chain. SNF uses graph composition and set theory to determine traffic classes handled by a service chain composed of multiple elements. It then synthesizes each traffic class using a minimal set of new elements that apply single-read-single-write and early-discard operations. Our SNF prototype takes a baseline state of the art network functions virtualization (NFV) framewor

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10

Krithika, V., Arshinder Kaur Dr., and K. Chandra Sekaran Dr. "Web Services Supply Chains: A Literature Review." International Journal on Web Service Computing (IJWSC) 3, no. 1 (2012): 15–29. https://doi.org/10.5281/zenodo.4060901.

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The globalization era has bought almost all services to the internet environment with multiple providers providing the same service in different ways. The same functionality is being offered by multiple providers so it becomes necessary to differentiate one self from the crowd in order to thrive against the cut throat competition from the service provider’s view. On the other hand it is absolutely essential for the client to select a service provider who meets not only the functional requirements of the client but also provides the best possible quality of service (QoS) to the customers.

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11

Moens, Hendrik, and Filip De Turck. "Customizable Function Chains: Managing Service Chain Variability in Hybrid NFV Networks." IEEE Transactions on Network and Service Management 13, no. 4 (2016): 711–24. http://dx.doi.org/10.1109/tnsm.2016.2580668.

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12

Liu, Weihua, Xuan Zhao, and Runze Wu. "Revenue-Sharing Contract Models for Logistics Service Supply Chains with Mass Customization Service." Mathematical Problems in Engineering 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/572624.

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The revenue-sharing contract is one of the most important supply chain coordination contracts; it has been applied in various supply chains. However, studies related to service supply chains with mass customization (MC) are lacking. Considering the equity of benefit distribution between the members of service supply chains, in this paper, we designed two revenue-sharing contracts. The first contract for the maximum equity of a single logistics service integrator (LSI) and single functional logistics service provider (FLSP) in a two-echelon logistics service supply chain was designed by introdu

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13

Xin, Baogui, Le Zhang, and Lei Xie. "Pricing decision of a dual-channel supply chain with different payment, corporate social responsibility and service level." RAIRO - Operations Research 56, no. 1 (2022): 49–75. http://dx.doi.org/10.1051/ro/2021187.

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Strategies such as price, CSR, and service have an important impact on enterprises and supply chains. This paper proposes a two-echelon dual-channel supply chain composed of a manufacturer and a retailer. Considering the product pricing, CSR level, and service level in the supply chain, this paper employs the Stackelberg game to depict supply chain participants’ optimal decisions and analyze the influence of explanatory variables on the optimal decision with retailer’s payment methods. The results state that market share, service level, CSR, and financing interest rate significantly impact the

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14

Khosravi, Habib Allah, and Yaghoub Farjami. "Machine Learning Based Classifier for Service Function Chains." Computing and Informatics 39, no. 3 (2020): 410–38. http://dx.doi.org/10.31577/cai_2020_3_410.

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15

Beck, Michael Till, and Juan Felipe Botero. "Scalable and coordinated allocation of service function chains." Computer Communications 102 (April 2017): 78–88. http://dx.doi.org/10.1016/j.comcom.2016.09.010.

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16

Wang, Yun, Chih-Kai Huang, Shan-Hsiang Shen, and Ge-Ming Chiu. "Adaptive Placement and Routing for Service Function Chains With Service Deadlines." IEEE Transactions on Network and Service Management 18, no. 3 (2021): 3021–36. http://dx.doi.org/10.1109/tnsm.2021.3086977.

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17

Pandey, Abhishek Kumar, and Sarvpal Singh. "Service Chain Placement by Using an African Vulture Optimization Algorithm Based VNF in Cloud-Edge Computing." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 12 (December 29, 2023): e31509. http://dx.doi.org/10.14201/adcaij.31509.

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The use of virtual network functions (VNFs) enables the implementation of service function chains (SFCs), which is an innovative approach for delivering network services. The deployment of service chains on the actual network infrastructure and the establishment of virtual connections between VNF instances are crucial factors that significantly impact the quality of network services provided. Current research on the allocation of vital VNFs and resource constraints on the edge network has overlooked the potential benefits of employing SFCs with instance reuse. This strategy offers significant

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18

Wang, Wei, Shengpeng Chen, Peiying Zhang, and Kai Liu. "Reinforcement-Learning-Assisted Service Function Chain Embedding Algorithm in Edge Computing Networks." Electronics 13, no. 15 (2024): 3007. http://dx.doi.org/10.3390/electronics13153007.

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Edge computing networks are critical infrastructures for processing massive data and providing instantaneous services. However, how to efficiently allocate resources in edge computing networks to meet the embedding requirements of service function chains has become an urgent problem. In this paper, we model the resource allocation problem in edge computing networks as a service function chain embedding problem model, aiming to optimize the resource allocation through reinforcement learning algorithms to achieve the goals of low cost, high revenue, and high embedding rate. In this paper, the ba

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19

Abdelaal, Marwa A., Gamal A. Ebrahim, and Wagdy R. Anis. "Efficient Placement of Service Function Chains in Cloud Computing Environments." Electronics 10, no. 3 (2021): 323. http://dx.doi.org/10.3390/electronics10030323.

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The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to con

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20

Sun, Jian, Guanhua Huang, Gang Sun, Hongfang Yu, Arun Kumar Sangaiah, and Victor Chang. "A Q-Learning-Based Approach for Deploying Dynamic Service Function Chains." Symmetry 10, no. 11 (2018): 646. http://dx.doi.org/10.3390/sym10110646.

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As the size and service requirements of today’s networks gradually increase, large numbers of proprietary devices are deployed, which leads to network complexity, information security crises and makes network service and service provider management increasingly difficult. Network function virtualization (NFV) technology is one solution to this problem. NFV separates network functions from hardware and deploys them as software on a common server. NFV can be used to improve service flexibility and isolate the services provided for each user, thus guaranteeing the security of user data. Therefore

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21

Cai, Jun, Zhongwei Huang, Liping Liao, Jianzhen Luo, and Wai-Xi Liu. "APPM: Adaptive Parallel Processing Mechanism for Service Function Chains." IEEE Transactions on Network and Service Management 18, no. 2 (2021): 1540–55. http://dx.doi.org/10.1109/tnsm.2021.3052223.

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22

Zhao, Dongcheng, Jing Ren, Rongping Lin, Shizhong Xu, and Victor Chang. "On Orchestrating Service Function Chains in 5G Mobile Network." IEEE Access 7 (2019): 39402–16. http://dx.doi.org/10.1109/access.2019.2895316.

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23

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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24

Kang, Rui, Fujun He, and Eiji Oki. "Robust Virtual Network Function Allocation in Service Function Chains With Uncertain Availability Schedule." IEEE Transactions on Network and Service Management 18, no. 3 (2021): 2987–3005. http://dx.doi.org/10.1109/tnsm.2021.3076511.

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25

Gausseran, Adrien, Andrea Tomassilli, Frederic Giroire, and Joanna Moulierac. "Don’t interrupt me when you reconfigure my Service Function Chains." Computer Communications 171 (April 2021): 39–53. http://dx.doi.org/10.1016/j.comcom.2021.02.008.

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26

Bi, Yu, Carlos Colman Meixner, Monchai Bunyakitanon, Xenofon Vasilakos, Reza Nejabati, and Dimitra Simeonidou. "Multi-Objective Deep Reinforcement Learning Assisted Service Function Chains Placement." IEEE Transactions on Network and Service Management 18, no. 4 (2021): 4134–50. http://dx.doi.org/10.1109/tnsm.2021.3127685.

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27

Mechtri, Marouen, Chaima Ghribi, and Djamal Zeghlache. "A Scalable Algorithm for the Placement of Service Function Chains." IEEE Transactions on Network and Service Management 13, no. 3 (2016): 533–46. http://dx.doi.org/10.1109/tnsm.2016.2598068.

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28

Dinh, NGOC-Thanh, and Younghan Kim. "An Efficient Reliability Guaranteed Deployment Scheme for Service Function Chains." IEEE Access 7 (2019): 46491–505. http://dx.doi.org/10.1109/access.2019.2908185.

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29

Tang, Keyin. "Research on Reliable Deployment Algorithm for Service Function Chain Based on Deep Reinforcement Learning." Innovation in Science and Technology 2, no. 6 (2023): 1–9. http://dx.doi.org/10.56397/ist.2023.11.01.

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This paper investigates the reliable deployment algorithm for Service Function Chains (SFC) based on deep reinforcement learning. SFC, as a chained function composition for complex network services, plays a crucial role in improving network efficiency and stability. To address the issue of existing SFC deployment algorithms that overlook the reliability of network functions and links, this paper proposes a deep reinforcement learning-based algorithm that utilizes a virtual network function and virtual link reliability mapping model for optimization. By learning the mapping between system state

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30

Khoshkholghi, Mohammad Ali, Michel Gokan Khan, Kyoomars Alizadeh Noghani, et al. "Service Function Chain Placement for Joint Cost and Latency Optimization." Mobile Networks and Applications 25, no. 6 (2020): 2191–205. http://dx.doi.org/10.1007/s11036-020-01661-w.

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AbstractNetwork Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge envi

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31

Yu, Heng, Zhilong Zheng, Junxian Shen, et al. "Octans: Optimal Placement of Service Function Chains in Many-Core Systems." IEEE Transactions on Parallel and Distributed Systems 32, no. 9 (2021): 2202–15. http://dx.doi.org/10.1109/tpds.2021.3063613.

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32

Zhai, Dong, Xiangru Meng, Zhenhua Yu, Hang Hu, and Xiaoyang Han. "A fine-grained and dynamic scaling method for service function chains." Knowledge-Based Systems 228 (September 2021): 107289. http://dx.doi.org/10.1016/j.knosys.2021.107289.

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33

Feng, Bohao, Guanglei Li, Guanwen Li, Yuming Zhang, Huachun Zhou, and Shui Yu. "Enabling Efficient Service Function Chains at Terrestrial-Satellite Hybrid Cloud Networks." IEEE Network 33, no. 6 (2019): 94–99. http://dx.doi.org/10.1109/mnet.2019.1800426.

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34

Liu, Bei, Shuting Long, and Xin Su. "Knowledge-Assisted Actor Critic Proximal Policy Optimization-Based Service Function Chain Reconfiguration Algorithm for 6G IoT Scenario." Entropy 26, no. 10 (2024): 820. http://dx.doi.org/10.3390/e26100820.

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Future 6G networks will inherit and develop Network Function Virtualization (NFV) architecture. With the NFV-enabled network architecture, it becomes possible to establish different virtual networks within the same infrastructure, create different Virtual Network Functions (VNFs) in different virtual networks, and form Service Function Chains (SFCs) that meet different service requirements through the orderly combination of VNFs. These SFCs can be deployed to physical entities as needed to provide network functions that support different services. To meet the highly dynamic service requirement

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35

Fulvio, Valenza, Sisto Riccardo, and Spinoso Serena. "Formally specifying and checking policies and anomalies in service function chaining." Journal of Network and Computer Applications Volume 146 (November 15, 2019): 1–14. https://doi.org/10.1016/j.jnca.2019.102419.

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One of the proposed management strategies for SDN networks is to specify traffic forwarding through policies, where each policy rule identifies a traffic flow and its traversed service chains. While network operators need to check network configurations as soon as possible, the SDN verification literature focuses on checking policy correctness during or after their deployment. This paper, instead, proposes early verification of forwarding policies before their deployment, by looking for the presence of anomalies that can potentially lead to erroneous and unexpected network behaviour. The propo

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36

Engelmann, Anna, and Admela Jukan. "A Combinatorial Reliability Analysis of Generic Service Function Chains in Data Center Networks." ACM Transactions on Modeling and Performance Evaluation of Computing Systems 6, no. 3 (2021): 1–24. http://dx.doi.org/10.1145/3477046.

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In data center networks, the reliability of Service Function Chain (SFC)—an end-to-end service presented by a chain of virtual network functions (VNFs)—is a complex and specific function of placement, configuration, and application requirements, both in hardware and software. Existing approaches to reliability analysis do not jointly consider multiple features of system components, including, (i) heterogeneity, (ii) disjointness, (iii) sharing, (iv) redundancy, and (v) failure interdependency. To this end, we develop a novel analysis of service reliability of the so-called generic SFC, consist

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Yao, Hong, Muzhou Xiong, Hui Li, Lin Gu, and Deze Zeng. "Joint optimization of function mapping and preemptive scheduling for service chains in network function virtualization." Future Generation Computer Systems 108 (July 2020): 1112–18. http://dx.doi.org/10.1016/j.future.2017.12.021.

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38

Xuan, Junlei, Huifang Yang, Xuelin Zhao, Xingpo Ma, and Xiaokai Yang. "Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment." Security and Communication Networks 2021 (October 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/7398206.

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Network function virtualization (NFV) has the potential to lead to significant reductions in capital expenditure and can improve the flexibility of the network. Virtual network function (VNF) deployment problem will be one of key problems that need to be addressed in NFV. To solve the problem of routing and VNF deployment, an optimization model, which minimizes the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF, is established. In this optimization model, the dependency among the different VNFs is considered. In order to solve the s

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39

Wu, You, Hefei Hu, and Ziyi Zhang. "DRL-Based Fast Joint Mapping Approach for SFC Deployment." Electronics 14, no. 12 (2025): 2408. https://doi.org/10.3390/electronics14122408.

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The rapid development of Network Function Virtualization (NFV) enables network operators to deliver customized end-to-end services through Service Function Chains (SFCs). However, existing two-stage deployment strategies fail to jointly optimize the placement of Virtual Network Functions (VNFs) and the routing of service traffic, resulting in inefficient resource utilization and increased service latency. This study addresses the challenge of maximizing the acceptance rate of service requests under resource constraints and latency requirements. We propose DRL-FJM, a novel dynamic SFC joint map

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40

Qi Xu, Deyun Gao, Huachun Zhou, Bohao Feng, and Taixin Li. "OpenNSP: Open Network Service Provision for Mobile Networks." Research Briefs on Information and Communication Technology Evolution 3 (May 15, 2017): 42–52. http://dx.doi.org/10.56801/rebicte.v3i.43.

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In recent years, many innovative techniques are emerged for satisfying various user requirements,such as Software Definition Network (SDN) and Network Function Virtualization (NFV). However,the current solutions for network service provision still lack enough flexibility and openness thatresult in high expenditures and complexity. Thus, we propose an Open Network Service Provision(OpenNSP) solution in this paper, aiming to address the multi-domain problems of service functionchain outsourcing for mobile networks. Particularly, OpenNSP is based on IETF hierarchical servicefunction chaining (hSF

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41

Manzanares-Lopez, Pilar, Juan Pedro Muñoz-Gea, and Josemaria Malgosa-Sanahuja. "VNF Placement for Service Function Chains with Strong Low-Delay Restrictions in Edge Computing Networks." Applied Sciences 10, no. 18 (2020): 6573. http://dx.doi.org/10.3390/app10186573.

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The edge computing paradigm, allowing the location of network services close to end users, defines new network scenarios. One of them considers the existence of micro data centers, with reduced resources but located closer to service requesters, to complement remote cloud data centers. This hierarchical and geo-distributed architecture allows the definition of different time constraints that can be taken into account when mapping services into data centers. This feature is especially useful in the Virtual Network Function (VNF) placement problem, where the network functions composing a Service

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Wang, Yangang, Hai Wang, Xianglin Wei, et al. "Service Function Chain Scheduling in Heterogeneous Multi-UAV Edge Computing." Drones 7, no. 2 (2023): 132. http://dx.doi.org/10.3390/drones7020132.

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Supporting Artificial Intelligence (AI)-enhanced intelligent applications on the resource-limited Unmanned Aerial Vehicle (UAV) platform is difficult due to the resource gap between the two. It is promising to partition an AI application into a service function (SF) chain and then dispatch the SFs onto multiple UAVs. However, it is still a challenging task to efficiently schedule the computation and communication resources of multiple UAVs to support a large number of SF chains (SFCs). Under the multi-UAV edge computing paradigm, this paper formulates the SFC scheduling problem as a 0–1 nonlin

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43

Huang, Zhongwei, Dagang Li, Chenhao Wu, and Hua Lu. "Reinforcement Learning-Based Delay-Aware Path Exploration of Parallelized Service Function Chains." Mathematics 10, no. 24 (2022): 4698. http://dx.doi.org/10.3390/math10244698.

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The parallel processing of the service function chain (SFC) is expected to provide better low-delay service delivery, because it breaks through the bottleneck of traditional serial processing mode in which service delay increases linearly with the SFC length. However, the provision of parallelized SFC (PSFC) is much more difficult due to the unique construction of PSFCs, inevitable parallelization overhead, and delay balancing requirement of PSFC branches; therefore, existing mechanisms for serial SFC cannot be directly applied to PSFC. After a comprehensive review of recent related work, we f

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44

Gunleifsen, Håkon, Thomas Kemmerich, and Vasileios Gkioulos. "A Proof-of-Concept Demonstration of Isolated and Encrypted Service Function Chains." Future Internet 11, no. 9 (2019): 183. http://dx.doi.org/10.3390/fi11090183.

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Contemporary Service Function Chaining (SFC), and the requirements arising from privacy concerns, call for the increasing integration of security features such as encryption and isolation across Network Function Virtualisation (NFV) domains. Therefore, suitable adaptations of automation and encryption concepts for the development of interconnected data centre infrastructures are essential. Nevertheless, packet isolation constraints related to the current NFV infrastructure and SFC protocols, render current NFV standards insecure. Accordingly, the goal of our work was an experimental demonstrat

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45

Aguado, A., D. R. López, A. Pastor, et al. "Quantum cryptography networks in support of path verification in service function chains." Journal of Optical Communications and Networking 12, no. 4 (2020): B9. http://dx.doi.org/10.1364/jocn.379799.

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46

Gour, Riti, Genya Ishigaki, Jian Kong, and Jason P. Jue. "Availability-guaranteed slice composition for service function chains in 5G transport networks." Journal of Optical Communications and Networking 13, no. 3 (2021): 14. http://dx.doi.org/10.1364/jocn.403721.

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47

Luo, Jianzhen, Jun Li, Lei Jiao, and Jun Cai. "On the Effective Parallelization and Near-Optimal Deployment of Service Function Chains." IEEE Transactions on Parallel and Distributed Systems 32, no. 5 (2021): 1238–55. http://dx.doi.org/10.1109/tpds.2020.3043768.

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48

Nogalski, Dariusz, Dallal Belabed, Alexandre Triollet, et al. "Critical Function Placement based on Service Chains in Multi-administrative Federated Networks." Journal of Communications Software and Systems 21, no. 1 (2025): 1–12. https://doi.org/10.24138/jcomss-2024-0117.

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49

Venâncio, Giovanni, Rogério C. Turchetti, and Elias Procópio Duarte Jr. "NFV-COIN: Unleashing The Power of In-Network Computing with Virtualization Technologies." Journal of Internet Services and Applications 12, no. 1 (2022): 46–53. http://dx.doi.org/10.5753/jisa.2022.2342.

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Network Functions Virtualization (NFV) allows the implementation in software of middleboxes traditionally available as specialized hardware. Network services can be implemented as SFCs (Service Function Chains) based onvirtualization technologies that run on commodity hardware. Although most virtualized functions have classic middlebox functionalities (e.g. firewalls or intrusion detectors) NFV technology can be used to leverage the networkto provide novel types of services to end-users. Actually, NFV can be very convenient to deploy traditional enduser services in the network, in the paradigm

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Sun, Jian, Yue Chen, Miao Dai, et al. "Energy Efficient Deployment of a Service Function Chain for Sustainable Cloud Applications." Sustainability 10, no. 10 (2018): 3499. http://dx.doi.org/10.3390/su10103499.

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Abstract:

With the increasing popularity of the Internet, user requests for cloud applications have dramatically increased. The traditional model of relying on dedicated hardware to implement cloud applications has not kept pace with the rapid growth in demand. Network function virtualization (NFV) architecture emerged at a historic moment. By moving the implementation of functions to software, a separation of functions and hardware was achieved. Therefore, when user demand increases, cloud application providers only need to update the software; the underlying hardware does not change, which can improve

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