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Статті в журналах з теми "Service Function Chain Placement"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

Khoshkholghi, Mohammad Ali, Michel Gokan Khan, Kyoomars Alizadeh Noghani, Javid Taheri, Deval Bhamare, Andreas Kassler, Zhengzhe Xiang, Shuiguang Deng, and Xiaoxian Yang. "Service Function Chain Placement for Joint Cost and Latency Optimization." Mobile Networks and Applications 25, no. 6 (November 21, 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 (June 14, 2019): 34. http://dx.doi.org/10.3390/jsan8020034.

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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 (January 30, 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 (September 30, 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, 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 (December 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 (September 2021): 3021–36. http://dx.doi.org/10.1109/tnsm.2021.3086977.

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8

Li, Wei, Yuan Jiang, Xiaoliang Zhang, Fangfang Dang, Feng Gao, Haomin Wang, and Qi Fan. "Reliability Assurance Dynamic SSC Placement Using Reinforcement Learning." Information 13, no. 2 (January 21, 2022): 53. http://dx.doi.org/10.3390/info13020053.

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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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9

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 (December 2021): 4134–50. http://dx.doi.org/10.1109/tnsm.2021.3127685.

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11

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 (September 2016): 533–46. http://dx.doi.org/10.1109/tnsm.2016.2598068.

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12

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 (September 20, 2020): 6573. http://dx.doi.org/10.3390/app10186573.

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Анотація:

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 Function Chain (SFC) may require more or less strong delay restrictions. We propose the ModPG (Modified Priority-based Greedy) heuristic, a VNF placement solution that weighs the latency, bandwidth, and resource restrictions, but also the instantiation cost of VNFs. ModPG is an improved solution of a previous proposal (called PG). Although both heuristics share the same optimization target, that is the reduction of the total substrate resource cost, the ModPG heuristic identifies and solves a limitation of the PG solution: the mapping of sets of SFCs that include a significant proportion of SFC requests with strong low-delay restrictions. Unlike PG heuristic performance evaluation, where the amount of SFC requests with strong low-delay restrictions is not considered as a factor to be analyzed, in this work, both solutions are compared considering the presence of 1%, 15%, and 25% of this type of SFC request. Results show that the ModPG heuristic optimizes the target cost similarly to the original proposal, and at the same time, it offers a better performance when a significant number of low-delay demanding SFC requests are present.

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13

Zu, Jiachen, Guyu Hu, Jiajie Yan, and Siqi Tang. "A community detection based approach for Service Function Chain online placement in data center network." Computer Communications 169 (March 2021): 168–78. http://dx.doi.org/10.1016/j.comcom.2021.01.014.

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14

XU, Yansen, and Ved P. KAFLE. "A Mathematical Model and Dynamic Programming Based Scheme for Service Function Chain Placement in NFV." IEICE Transactions on Information and Systems E102.D, no. 5 (May 1, 2019): 942–51. http://dx.doi.org/10.1587/transinf.2018ntp0015.

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15

Wu, Xing, Jing Duan, Mingyu Zhong, Peng Li, and Jianjia Wang. "VNF Chain Placement for Large Scale IoT of Intelligent Transportation." Sensors 20, no. 14 (July 8, 2020): 3819. http://dx.doi.org/10.3390/s20143819.

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With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation.

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16

Yu, Heng, Zhilong Zheng, Junxian Shen, Congcong Miao, Chen Sun, Hongxin Hu, Jun Bi, Jianping Wu, and Jilong Wang. "Octans: Optimal Placement of Service Function Chains in Many-Core Systems." IEEE Transactions on Parallel and Distributed Systems 32, no. 9 (September 1, 2021): 2202–15. http://dx.doi.org/10.1109/tpds.2021.3063613.

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17

Magoula, Lina, Sokratis Barmpounakis, Ioannis Stavrakakis, and Nancy Alonistioti. "A genetic algorithm approach for service function chain placement in 5G and beyond, virtualized edge networks." Computer Networks 195 (August 2021): 108157. http://dx.doi.org/10.1016/j.comnet.2021.108157.

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18

Elagin, Vasily S., Alexander V. Bogachev, and Ilya A. Belozertsev. "Modeling the estimation of end-to-end packet latency for a chain of NFV nodes in 5G networks." T-Comm 16, no. 3 (2022): 23–30. http://dx.doi.org/10.36724/2072-8735-2022-16-3-23-30.

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It is expected that future communication networks will provide configurable delay-sensitive types of services (for example, streaming video, machine interaction). To support a variety of applications and use cases of servers providing various functions, you can use network function virtualization (NFV), which will be able to provide flexible implementation and placement of configuration of the necessary network functions. This article analyzes the end-to-end packet latency (E2E) for multiple traffic flows passing through the chain of embedded virtual network functions (VNF) in fifth-generation communication networks (5G). The Dominant of Generalized Resource Processing (DR-GPS) is used to distribute computing resources and transfer data between threads in each node of Network Function Virtualization (NFV) to achieve equitable distribution and utilization of available resources. The tandem queuing model is designed for incoming packets combined in several streams passing through the NFV node and its outgoing transmission channel. To analyze manageability, we separate packet processing (and transmission) of various streams in the simulation and determine the average packet processing and transmission rates of each stream as approximate service speeds.

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19

Alharbe, Nawaf, Abeer Aljohani, and Mohamed Ali Rakrouki. "A Fuzzy Grouping Genetic Algorithm for Solving a Real-World Virtual Machine Placement Problem in a Healthcare-Cloud." Algorithms 15, no. 4 (April 14, 2022): 128. http://dx.doi.org/10.3390/a15040128.

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Due to the large-scale development of cloud computing, data center electricity energy costs have increased rapidly. Energy saving has become a major research direction of virtual machine placement problems. At the same time, the multi-dimensional resources on the cloud should be used in a balanced manner in order to avoid resources waste. In this context, this paper addresses a real-world virtual machine placement problem arising in a Healthcare-Cloud (H-Cloud) of hospitals chain in Saudi Arabia, considering server power consumption and resource utilization. As a part of optimizing both objectives, user service quality has to be taken into account. In fact, user quality of service (QoS) is also considered by measuring the Service-Level Agreement (SLA) violation rate. This problem is modeled as a multi-objective virtual machine placement problem with the objective of minimizing power consumption, resource utilization, and SLA violation rate. To solve this challenging problem, a fuzzy grouping genetic algorithm (FGGA) is proposed. Considering that multiple optimization objectives may have different degrees of influence on the problem, the fitness function of the proposed algorithm is calculated with fuzzy logic-based function. The experimental results show the effectiveness of the proposed algorithm.

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20

Bolumole, Yemisi A., A. Michael Knemeyer, and Douglas M. Lambert. "The Customer Service Management Process." International Journal of Logistics Management 14, no. 2 (July 1, 2003): 15–31. http://dx.doi.org/10.1108/09574090310806576.

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Customer service management is the supply chain management process that represents the firm's face to the customer. The process is the key point of contact for administering product and service agreements (PSAs) developed by customer teams as part of the customer relationship management process. The goal is to provide a single source of customer information, such as product availability, shipping dates and order status. Customer service management requires a real‐time system to respond to customer inquiries and facilitate order placement. In this paper, we describe the customer service management process in detail to demonstrate how it can be implemented and managed. To do this, we detail the activities of each strategic and operational sub‐process; evaluate the interfaces with the business functions, the other seven supply chain management processes; and describe examples of successful implementation.

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21

Pham, Tuan-Minh, and Thi-Minh Nguyen. "Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles." Sensors 21, no. 24 (December 17, 2021): 8446. http://dx.doi.org/10.3390/s21248446.

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The massive amount of data generated daily by various sensors equipped with connected autonomous vehicles (CAVs) can lead to a significant performance issue of data processing and transfer. Network Function Virtualization (NFV) is a promising approach to improving the performance of a CAV system. In an NFV framework, Virtual Network Function (VNF) instances can be placed in edge and cloud servers and connected together to enable a flexible CAV service with low latency. However, protecting a service function chain composed of several VNFs from a failure is challenging in an NFV-based CAV system (VCAV). We propose an integer linear programming (ILP) model and two approximation algorithms for resilient services to minimize the service disruption cost in a VCAV system when a failure occurs. The ILP model, referred to as TERO, allows us to obtain the optimal solution for traffic engineering, including the VNF placement and routing for resilient services with regard to dynamic routing. Our proposed algorithms based on heuristics (i.e., TERH) and reinforcement learning (i.e., TERA) provide an approximation solution for resilient services in a large-scale VCAV system. Evaluation results with real datasets and generated network topologies show that TERH and TERA can provide a solution close to the optimal result. It also suggests that TERA should be used in a highly dynamic VCAV system.

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22

Pei, Jianing, Peilin Hong, Kaiping Xue, and Defang Li. "Efficiently Embedding Service Function Chains with Dynamic Virtual Network Function Placement in Geo-Distributed Cloud System." IEEE Transactions on Parallel and Distributed Systems 30, no. 10 (October 1, 2019): 2179–92. http://dx.doi.org/10.1109/tpds.2018.2880992.

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23

Ruiz, Lidia, Ramón Durán, Ignacio de Miguel, Pouria Khodashenas, Jose-Juan Pedreño-Manresa, Noemí Merayo, Juan Aguado, et al. "A Genetic Algorithm for VNF Provisioning in NFV-Enabled Cloud/MEC RAN Architectures." Applied Sciences 8, no. 12 (December 13, 2018): 2614. http://dx.doi.org/10.3390/app8122614.

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5G technologies promise to bring new network and service capacities and are expected to introduce significant architectural and service deployment transformations. The Cloud-Radio Access Networks (C-RAN) architecture, enabled by the combination of Software Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) technologies, play a key role in the development of 5G. In this context, this paper addresses the problems of Virtual Network Functions (VNF) provisioning (VNF-placement and service chain allocation) in a 5G network. In order to solve that problem, we propose a genetic algorithm that, considering both computing resources and optical network capacity, minimizes both the service blocking rate and CPU usage. In addition, we present an algorithm extension that adds a learning stage and evaluate the algorithm performance benefits in those scenarios where VNF allocations can be reconfigured. Results reveal and quantify the advantages of reconfiguring the VNF mapping depending on the current demands. Our methods outperform previous proposals in the literature, reducing the service blocking ratio while saving energy by reducing the number of active core CPUs.

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24

Thiruvasagam, Prabhu Kaliyammal, Abhishek Chakraborty, Abin Mathew, and C. Siva Ram Murthy. "Reliable Placement of Service Function Chains and Virtual Monitoring Functions With Minimal Cost in Softwarized 5G Networks." IEEE Transactions on Network and Service Management 18, no. 2 (June 2021): 1491–507. http://dx.doi.org/10.1109/tnsm.2021.3056917.

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25

Djekic, Ilija, Dubravka Skunca, Ivan Nastasijevic, Vladimir Tomovic, and Igor Tomasevic. "Transformation of quality aspects throughout the chicken meat supply chain." British Food Journal 120, no. 5 (May 8, 2018): 1132–50. http://dx.doi.org/10.1108/bfj-08-2017-0432.

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Анотація:

Purpose The purpose of this paper is to analyze perceptions of quality in the chicken meat supply chain. Design/methodology/approach This survey covered 74 different farms, slaughterhouses, meat processors, and retailers and 500 consumers, using two methods. From the farm to retail, analysis covered “customer – supplier” viewpoints in different stages of the supply chain. From the consumers to the farms, the quality function deployment method was used. Five houses of quality have been developed using the Delphi method to synthesize the opinions of experts. Findings Farm-slaughter comparison shows that final weight at farm gate and animal welfare are the most important quality attributes. The quality aspect important for slaughterhouses and meat processors is the cold chain. Retailers and meat processors highlight the portfolio of various chicken meat products as their most important quality attribute. At the points of sale, shelf illumination and product placement are prevailing. Research limitations/implications The results suggest that there are different views on quality by all actors in the supply chain, from quality of chicken meat, food safety and quality of service in retail to profitability and animal welfare. Practical implications The paper enhances simplicity in analyzing quality aspects of different types of meat supply chains. This methodology enables a synergy of value chain promotion with other quality development approaches. It also creates possibilities for policy makers to improve competitiveness strategies. Originality/value Application of a similar approach to other parts of the food chain could offer a better insight into the transformation of quality.

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26

Gatzianas, Marios, Agapi Mesodiakaki, George Kalfas, Nikos Pleros, Francesca Moscatelli, Giada Landi, Nicola Ciulli, and Leonardo Lossi. "Offline Joint Network and Computational Resource Allocation for Energy-Efficient 5G and beyond Networks." Applied Sciences 11, no. 22 (November 9, 2021): 10547. http://dx.doi.org/10.3390/app112210547.

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Анотація:

In order to cope with the ever-increasing traffic demands and stringent latency constraints, next generation, i.e., sixth generation (6G) networks, are expected to leverage Network Function Virtualization (NFV) as an enabler for enhanced network flexibility. In such a setup, in addition to the traditional problems of user association and traffic routing, Virtual Network Function (VNF) placement needs to be jointly considered. To that end, in this paper, we focus on the joint network and computational resource allocation, targeting low network power consumption while satisfying the Service Function Chain (SFC), throughput, and delay requirements. Unlike the State-of-the-Art (SoA), we also take into account the Access Network (AN), while formulating the problem as a general Mixed Integer Linear Program (MILP). Due to the high complexity of the proposed optimal solution, we also propose a low-complexity energy-efficient resource allocation algorithm, which was shown to significantly outperform the SoA, by achieving up to 78% of the optimal energy efficiency with up to 742 times lower complexity. Finally, we describe an Orchestration Framework for the automated orchestration of vertical-driven services in Network Slices and describe how it encompasses the proposed algorithm towards optimized provisioning of heterogeneous computation and network resources across multiple network segments.

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Dinh, Ngoc-Thanh, and Younghan Kim. "An Efficient Availability Guaranteed Deployment Scheme for IoT Service Chains over Fog-Core Cloud Networks." Sensors 18, no. 11 (November 15, 2018): 3970. http://dx.doi.org/10.3390/s18113970.

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High availability is one of the important requirements of many end-to-end services in the Internet of Things (IoT). This is a critical issue in network function virtualization (NFV) and NFV-enabled service function chaining (SFC) due to hard- and soft-ware failures. Thus, merely mapping primary VNFs is not enough to ensure high availability, especially for SFCs deployed over fog - core cloud networks due to resource limitations of fogs. As a result, additional protection schemes, like VNF redundancy deployments, are required to improve the availability of SFCs to meet predefined requirements. With limited resources of fog instances, a cost-efficient protection scheme is required. This paper proposes a cost-efficient availability guaranteed deployment scheme for IoT services over fog-core cloud networks based on measuring the improvement potential of VNFs for improving the availability of SFCs. In addition, various techniques for redundancy placement for VNFs at the fog layer are also presented. Obtained analysis and simulation results show that the proposed scheme achieves a significant improvement in terms of the cost efficiency and scalability compared to the state-of-the-art approaches.

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Li, Jing, Weifa Liang, Zichuan Xu, Xiaohua Jia, and Wanlei Zhou. "Service Provisioning for Multi-source IoT Applications in Mobile Edge Computing." ACM Transactions on Sensor Networks 18, no. 2 (May 31, 2022): 1–25. http://dx.doi.org/10.1145/3484200.

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Анотація:

We are embracing an era of Internet of Things (IoT). The latency brought by unstable wireless networks caused by limited resources of IoT devices seriously impacts the quality of services of users, particularly the service delay they experienced. Mobile Edge Computing (MEC) technology provides promising solutions to delay-sensitive IoT applications, where cloudlets (edge servers) are co-located with wireless access points in the proximity of IoT devices. The service response latency for IoT applications can be significantly shortened due to that their data processing can be performed in a local MEC network. Meanwhile, most IoT applications usually impose Service Function Chain (SFC) enforcement on their data transmission, where each data packet from its source gateway of an IoT device to the destination (a cloudlet) of the IoT application must pass through each Virtual Network Function (VNF) in the SFC in an MEC network. However, little attention has been paid on such a service provisioning of multi-source IoT applications in an MEC network with SFC enforcement. In this article, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements and aiming at minimizing the cost of such service provisioning, where each IoT application has multiple data streams from different sources to be uploaded to a location (cloudlet) in the MEC network for aggregation, processing, and storage purposes. To this end, we first formulate two novel optimization problems: the cost minimization problem of service provisioning for a single multi-source IoT application, and the service provisioning problem for a set of multi-source IoT applications, respectively, and show that both problems are NP-hard. Second, we propose a service provisioning framework in the MEC network for multi-source IoT applications that consists of uploading stream data from multiple sources of the IoT application to the MEC network, data stream aggregation and routing through the VNF instance placement and sharing, and workload balancing among cloudlets. Third, we devise an efficient algorithm for the cost minimization problem built upon the proposed service provisioning framework, and further extend the solution for the service provisioning problem of a set of multi-source IoT applications. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.

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29

Kutiel, Gilad, and Dror Rawitz. "Service chain placement in SDNs." Discrete Applied Mathematics 270 (November 2019): 168–80. http://dx.doi.org/10.1016/j.dam.2019.06.013.

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30

Hmaity, Ali, Marco Savi, Francesco Musumeci, Massimo Tornatore, and Achille Pattavina. "Protection strategies for virtual network functions placement and service chains provisioning." Networks 70, no. 4 (September 28, 2017): 373–87. http://dx.doi.org/10.1002/net.21782.

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31

Zhu, Kai, Chunming Wu, and Boyang Zhou. "Scheduling Reconfigurable Service Function Chain." Chinese Journal of Electronics 28, no. 6 (November 1, 2019): 1244–49. http://dx.doi.org/10.1049/cje.2019.08.011.

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32

Jang, Insun, Dongeun Suh, Sangheon Pack, and Gyorgy Dan. "Joint Optimization of Service Function Placement and Flow Distribution for Service Function Chaining." IEEE Journal on Selected Areas in Communications 35, no. 11 (November 2017): 2532–41. http://dx.doi.org/10.1109/jsac.2017.2760162.

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33

Huin, Nicolas, Andrea Tomassilli, Frédéric Giroire, and Brigitte Jaumard. "Energy-Efficient Service Function Chain Provisioning." Journal of Optical Communications and Networking 10, no. 3 (February 6, 2018): 114. http://dx.doi.org/10.1364/jocn.10.000114.

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34

Huin, Nicolas, Andrea Tomassilli, Frédéric Giroire, and Brigitte Jaumard. "Energy-Efficient Service Function Chain Provisioning." Electronic Notes in Discrete Mathematics 64 (February 2018): 265–74. http://dx.doi.org/10.1016/j.endm.2018.02.001.

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35

Tassiulas, Leandros. "Enabling Intelligent Services at the Network Edge." ACM SIGMETRICS Performance Evaluation Review 49, no. 1 (June 22, 2022): 69–70. http://dx.doi.org/10.1145/3543516.3453912.

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Анотація:

The proliferation of novel mobile applications and the associated AI services necessitates a fresh view on the architecture, algorithms and services at the network edge in order to meet stringent performance requirements. Some recent work addressing these challenges is presented. In order to meet the requirement for low-latency, the execution of computing tasks moves form the cloud to the network edge, closer to the end-users. The joint optimization of service placement and request routing in dense mobile edge computing networks is considered. Multidimensional constraints are introduced to capture the storage requirements of the vast amounts of data needed. An algorithm that achieves close-to-optimal performance using a randomized rounding technique is presented. Recent advances in network virtualization and programmability enable realization of services as chains, where flows can be steered through a pre-defined sequence of functions deployed at different network locations. The optimal deployment of such service chains where storage is a stringent constraint in addition to computation and bandwidth is considered and an approximation algorithm with provable performance guarantees is proposed and evaluated. Finally the problem of traffic flow classification as it arises in firewalls and intrusion detection applications is presented. An approach for realizing such functions based on a novel two-stage deep learning method for attack detection is presented. Leveraging the high level of data plane programmability in modern network hardware, the realization of these mechanisms at the network edge is demonstrated.

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36

Fan, Weiqi, Qimei Cui, Xiangjun Li, Xueqing Huang, and Xiaofeng Tao. "On Credibility-Based Service Function Chain Deployment." IEEE Open Journal of the Computer Society 2 (2021): 152–63. http://dx.doi.org/10.1109/ojcs.2021.3064887.

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37

Siasi, Nazli, Mohammed Jasim, and Nasir Ghani. "Service Function Chain Mapping in Fog Networks." IEEE Communications Letters 25, no. 1 (January 2021): 99–102. http://dx.doi.org/10.1109/lcomm.2020.3019207.

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38

Zhong, Xuxia, Ying Wang, and Xuesong Qiu. "Service function chain orchestration across multiple clouds." China Communications 15, no. 10 (October 2018): 99–116. http://dx.doi.org/10.1109/cc.2018.8485473.

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39

Bradley, Steve. "A case-study of the Careers Service placement function." British Journal of Guidance and Counselling 20, no. 1 (January 1, 1992): 90–107. http://dx.doi.org/10.1080/03069889200760081.

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40

Bradley, Steve. "A Case-Study of the Careers Service Placement Function." British Journal of Guidance & Counselling 20, no. 1 (January 1992): 90–107. http://dx.doi.org/10.1080/03069889208253612.

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41

Qiu, Hang, Hongbo Tang, Wei You, Mingyan Xu, and Kai Wang. "Robust service provisioning of service function chain under demand uncertainty." IET Communications 16, no. 7 (April 2022): 803–14. http://dx.doi.org/10.1049/cmu2.12387.

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42

Mutichiro, Briytone, and Younghan Kim. "User preference–based QoS-aware service function placement in IoT-Edge cloud." International Journal of Distributed Sensor Networks 17, no. 5 (May 2021): 155014772110199. http://dx.doi.org/10.1177/15501477211019912.

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Анотація:

In the Internet of Things-Edge cloud, service provision presents a challenge to operators to satisfy user service-level agreements while meeting service-specific quality-of-service requirements. This is because of inherent limitations in the Internet of Things-Edge in terms of resource infrastructure as well as the complexity of user requirements in terms of resource management in a heterogeneous environment like edge. An efficient solution to this problem is service orchestration and placement of service functions to meet user-specific requirements. This work aims to satisfy user quality of service through optimizing the user response time and cost by factoring in the workload variation on the edge infrastructure. We formulate the service function placement at the edge problem. We employ user service request patterns in terms of user preference and service selection probability to model service placement. Our framework proposal relies on mixed-integer linear programming and heuristic solutions. The main objective is to realize a reduced user response time at minimal overall cost while satisfying the user service requirements. For this, several parameters, and factors such as capacity, latency, workload, and cost constraints, are considered. The proposed solutions are evaluated based on different metrics and the obtained results show the gap between the heuristic user preference placement algorithm and the optimal solution to be minimal.

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43

Bhamare, Deval, Mohammed Samaka, Aiman Erbad, Raj Jain, Lav Gupta, and H. Anthony Chan. "Optimal virtual network function placement in multi-cloud service function chaining architecture." Computer Communications 102 (April 2017): 1–16. http://dx.doi.org/10.1016/j.comcom.2017.02.011.

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44

Yue, Qiang, Xiangtao Liu, Lijia Fang, Xiaoxiao Wang, and Wenbin Hu. "A Container Service Chain Placement Greedy Algorithm Based on Heuristic Information." Journal of Physics: Conference Series 1621 (August 2020): 012068. http://dx.doi.org/10.1088/1742-6596/1621/1/012068.

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45

Xie, Yanghao, Sheng Wang, and Yueyue Dai. "Revenue-maximizing virtualized network function chain placement in dynamic environment." Future Generation Computer Systems 108 (July 2020): 650–61. http://dx.doi.org/10.1016/j.future.2020.03.011.

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46

Zhai, Dong, Xiangru Meng, Zhenhua Yu, and Xiaoyang Han. "Reliability-Aware Service Function Chain Backup Protection Method." IEEE Access 9 (2021): 14660–76. http://dx.doi.org/10.1109/access.2021.3051045.

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47

Liu, Junjie, Wei Lu, Fen Zhou, Ping Lu, and Zuqing Zhu. "On Dynamic Service Function Chain Deployment and Readjustment." IEEE Transactions on Network and Service Management 14, no. 3 (September 2017): 543–53. http://dx.doi.org/10.1109/tnsm.2017.2711610.

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48

Bari, Md Faizul, Shihabur Rahman Chowdhury, and Raouf Boutaba. "ESSO: An Energy Smart Service Function Chain Orchestrator." IEEE Transactions on Network and Service Management 16, no. 4 (December 2019): 1345–59. http://dx.doi.org/10.1109/tnsm.2019.2944170.

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49

Cheng, Guozhen, Hongchang Chen, Hongchao Hu, Zhiming Wang, and Julong Lan. "Enabling network function combination via service chain instantiation." Computer Networks 92 (December 2015): 396–407. http://dx.doi.org/10.1016/j.comnet.2015.09.015.

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50

Hawilo, Hassan, Manar Jammal, and Abdallah Shami. "Network Function Virtualization-Aware Orchestrator for Service Function Chaining Placement in the Cloud." IEEE Journal on Selected Areas in Communications 37, no. 3 (March 2019): 643–55. http://dx.doi.org/10.1109/jsac.2019.2895226.

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