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Статті в журналах з теми "Service Function Chain Placement"

1

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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Анотація:
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 environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).
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2

Dang-Quang, Nhat-Minh, and Myungsik Yoo. "Optimized placement of symmetrical service function chain in network function virtualization." Computer Science and Information Systems, no. 00 (2022): 6. http://dx.doi.org/10.2298/csis210920006d.

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Анотація:
Network function virtualization (NFV) is one of the key technology enablers for actualizing 5G networks. With NFV, virtual network functions (VNFs) are linked together as a service function chain (SFC), which provides network functionality for the customer on demand. However, how to efficiently find a suitable placement for VNFs regarding the given objectives is an extremely difficult issue. The existing approaches assume that the SFC has a simple and asymmetrical pattern that is unsuitable to modeling a real system. We address this limitation by studying a VNF placement optimization problem with symmetrical SFCs that can support both symmetric and asymmetric traffic flows. This NP-hard problem is formulated as a mixed-integer linear programming (MILP) model. An iterative greedy-based heuristic is proposed to overcome the complexity of the MILP model. Extensive simulation results show that the proposed heuristic can obtain a near-optimal solution compared to MILP for a small-scale network, and at the same time, is superior to a traditional heuristic for a large-scale network.
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3

Xu, Yansen, and Ved P. Kafle. "An Availability-Enhanced Service Function Chain Placement Scheme in Network Function Virtualization." Journal of Sensor and Actuator Networks 8, no. 2 (2019): 34. http://dx.doi.org/10.3390/jsan8020034.

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Анотація:
A service function chain (SFC) is an ordered virtual network function (VNF) chain for processing traffic flows to deliver end-to-end network services in a virtual networking environment. A challenging problem for an SFC in this context is to determine where to deploy VNFs and how to route traffic between VNFs of an SFC on a substrate network. In this paper, we formulate an SFC placement problem as an integer linear programing (ILP) model, and propose an availability-enhanced VNF placing scheme based on the layered graphs approach. To improve the availability of SFC deployment, our scheme distributes VNFs of an SFC to multiple substrate nodes to avoid a single point of failure. We conduct numerical analysis and computer simulation to validate the feasibility of our SFC scheme. The results show that the proposed scheme outperforms well in different network scenarios in terms of end-to-end delay of the SFC and computation time cost.
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4

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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Анотація:
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 consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.
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5

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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Анотація:
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, consisting of n = k + r sub-SFCs, whereby k ≥ 1 and r ≥ 0 are the numbers of arbitrary placed primary and backup (redundant) sub-SFCs, respectively. Our analysis is based on combinatorics and a reduced binomial theorem—resulting in a simple approach, which, however, can be utilized to analyze rather complex SFC configurations. The analysis is practically applicable to various VNF placement strategies in arbitrary data center configurations, and topologies and can be effectively used for evaluation and optimization of reliable SFC placements.
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6

Wang, Zenan, Jiao Zhang, Tao Huang, and Yunjie Liu. "Service Function Chain Composition, Placement, and Assignment in Data Centers." IEEE Transactions on Network and Service Management 16, no. 4 (2019): 1638–50. http://dx.doi.org/10.1109/tnsm.2019.2933872.

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7

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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8

Li, Wei, Yuan Jiang, Xiaoliang Zhang, et al. "Reliability Assurance Dynamic SSC Placement Using Reinforcement Learning." Information 13, no. 2 (2022): 53. http://dx.doi.org/10.3390/info13020053.

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Анотація:
Software-defined networking (SDN) and network function virtualization (NFV) make a network programmable, resulting in a more flexible and agile network. An important and promising application for these two technologies is network security, where they can dynamically chain virtual security functions (VSFs), such as firewalls, intrusion detection systems, and intrusion prevention systems, and thus inspect, monitor, or filter traffic flows in cloud data center networks. In view of the strict delay constraints of security services and the high failure probability of VSFs, we propose the use of a security service chain (SSC) orchestration algorithm that is latency aware with reliability assurance (LARA). This algorithm includes an SSC orchestration module and VSF backup module. We first use a reinforcement learning (RL) based Q-learning algorithm to achieve efficient SSC orchestration and try to reduce the end-to-end delay of services. Then, we measure the importance of the physical nodes carrying the VSF instance and backup VSF according to the node importance of VSF. Extensive simulation results indicate that the LARA algorithm is more effective in reducing delay and ensuring reliability compared with other algorithms.
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Subramanya, Tejas, Davit Harutyunyan, and Roberto Riggio. "Machine learning-driven service function chain placement and scaling in MEC-enabled 5G networks." Computer Networks 166 (January 2020): 106980. http://dx.doi.org/10.1016/j.comnet.2019.106980.

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10

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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