Adaptive service function selection for Network Function Virtualization networking

2019 ◽  
Vol 91 ◽  
pp. 108-123 ◽  
Author(s):  
Yi-Wei Ma ◽  
Jiann-Liang Chen ◽  
Jia-Yi Jhou
Keyword(s):  
Network Function Virtualization ◽  
Service Function ◽  
Network Function ◽  
Selection For ◽  
Function Selection

scholarly journals Dynamic Service Function Chaining Orchestration in a Multi-Domain: A Heuristic Approach Based on SRv6

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6563
Author(s):  
Yutong Wu ◽  
Jinhe Zhou
Keyword(s):  
Virtual Machines ◽  
Search Algorithm ◽  
Resource Consumption ◽  
Network Function Virtualization ◽  
Ant Colony Optimization Algorithm ◽  
Network Resource ◽  
Load Pressure ◽  
Service Function ◽  
Network Function ◽  
Series Of Experiments

With the emergence of virtualization technology, Network Function Virtualization (NFV) and Software Defined Networking (SDN) make the network function abstract from the hardware and allow it to be run on virtual machines. These technologies can help to provide more efficient services to users by Service Function Chaining (SFC). The sequence of multiple VNFs required by network operators to perform traffic steering is called SFC. Mapping and deploying SFC on the physical network can enable users to obtain customized services in time. At present, a key problem in deploying SFC is how to reduce network resource consumption and load pressure while ensuring the corresponding services for users. In this paper, we first introduce an NFV architecture for SFC deployment, and illustrate the SFC orchestration process which is based on SRv6 in multi-domain scenario. Then, we propose an effective SFC dynamic orchestration algorithm. First, we use Breadth-First Search algorithm to traverse network and find the shortest path for deploying VNFs. Next, we use the improved Ant Colony Optimization algorithm to generate the optimal deployment scheme. Finally, we conduct a series of experiments to verify the performance of our algorithm. Compared with other deployment algorithms, the results show that our solution effectively optimizes end-to-end delay, bandwidth resource consumption and load balancing.

scholarly journals Path Load Adaptive Migration for Routing and Bandwidth Allocation in Mobile-Aware Service Function Chain

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 57
Author(s):  
Hefei Hu ◽  
Chen Yang ◽  
Lingyi Xu ◽  
Tangyijia Song ◽  
Bonaho Bocochi Dalia
Keyword(s):  
Failure Rate ◽  
Bandwidth Allocation ◽  
Arrival Time ◽  
Network Function Virtualization ◽  
Estimation Model ◽  
Migration Strategy ◽  
Service Function ◽  
Network Function ◽  
Service Function Chain

With network function virtualization (NFV) expanding from network center to edge, the service function chain (SFC) will gradually approach users to provide lower delay and higher-quality services. User mobility seriously affects the quality of service (QoS) provided by the mobile-aware SFC. Therefore, we must migrate the SFC to provide continuous services. In the user estimable movement scenario with a known mobile path and estimable arrival time, we establish the estimation model of user arrival time to obtain the estimated arrival time. Then, to reduce the time that the user is waiting for the migration completion, we propose a softer migration strategy migrating mobile-aware SFC before the user arrives at the corresponding access node. Moreover, for the problem of routing and bandwidth allocation (RBA), to reduce the migration failure rate, the paper proposes a path load adaptive routing and bandwidth allocation (PLARBA) algorithm adjusting the migration bandwidth according to the path load. The experimental results show that the proposed algorithm has significant advantages in reducing the user’s waiting time by more than 90%, decreasing migration failure rate by up to 75%, and improving QoS compared to the soft migration strategy and two RBA algorithms.

scholarly journals Low-Latency and Resource-Efficient Service Function Chaining Orchestration in Network Function Virtualization

2020 ◽  
Vol 7 (7) ◽  
pp. 5760-5772 ◽  
Author(s):  
Gang Sun ◽  
Zhu Xu ◽  
Hongfang Yu ◽  
Xi Chen ◽  
Victor Chang ◽  
...  
Keyword(s):  
Network Function Virtualization ◽  
Low Latency ◽  
Service Function ◽  
Network Function

scholarly journals An Availability-Enhanced Service Function Chain Placement Scheme in Network Function Virtualization

2019 ◽  
Vol 8 (2) ◽  
pp. 34
Author(s):  
Yansen Xu ◽  
Ved P. Kafle
Keyword(s):  
Single Point ◽  
Computation Time ◽  
Network Services ◽  
Network Function Virtualization ◽  
Placement Problem ◽  
Service Function ◽  
Network Function ◽  
Multiple Substrate ◽  
Service Function Chain ◽  
End To End

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.

scholarly journals Energy Efficient Deployment of a Service Function Chain for Sustainable Cloud Applications

2018 ◽  
Vol 10 (10) ◽  
pp. 3499 ◽  
Author(s):  
Jian Sun ◽  
Yue Chen ◽  
Miao Dai ◽  
Wanting Zhang ◽  
Arun Sangaiah ◽  
...  
Keyword(s):  
Power Consumption ◽  
Greedy Algorithms ◽  
Load Ratio ◽  
Research Problem ◽  
Network Function Virtualization ◽  
Service Function ◽  
Acceptance Ratio ◽  
Network Function ◽  
Service Function Chain ◽  
Cloud Applications

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 network scalability. Although NFV solves the problem of network expansion, deploying service function chains into the underlying network to optimize indicators remains an important research problem that requires consideration of delay, reliability, and power consumption. In this paper, we consider the optimization of power consumption with the premise of guaranteeing a certain virtual function link yield. We propose an efficient algorithm that is based on first-fit and greedy algorithms to solve the problem. The simulation results show that the proposed algorithm substantially improves the path-finding efficiency, achieves a higher request acceptance ratio and reduces power consumption while provisioning the cloud applications. Compared with the baseline algorithm, the service function chain (SFC) acceptance ratio of our proposed algorithms improves by a maximum of approximately 15%, our proposed algorithm reduces the power consumption by a maximum of approximately 15%, the average link load ratio of our proposed algorithm reduces by a maximum of approximately 20%, and the average mapped path length of our proposed algorithm reduces by a maximum of approximately 1.5 hops.

scholarly journals Application of Machine Learning Algorithms for Traffic Forecasting in Dynamic Optical Networks with Service Function Chains

2020 ◽  
Vol 45 (3) ◽  
pp. 217-232
Author(s):  
Daniel Szostak ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Machine Learning Algorithms ◽  
Network Function Virtualization ◽  
Service Function ◽  
Traffic Forecasting ◽  
Network Function ◽  
Traffic Characteristics

AbstractKnowledge about future optical network traffic can be beneficial for network operators in terms of decreasing an operational cost due to efficient resource management. Machine Learning (ML) algorithms can be employed for forecasting traffic with high accuracy. In this paper we describe a methodology for predicting traffic in a dynamic optical network with service function chains (SFC). We assume that SFC is based on the Network Function Virtualization (NFV) paradigm. Moreover, other type of traffic, i.e. regular traffic, can also occur in the network. As a proof of effectiveness of our methodology we present and discuss numerical results of experiments run on three benchmark networks. We examine six ML classifiers. Our research shows that it is possible to predict a future traffic in an optical network, where SFC can be distinguished. However, there is no one universal classifier that can be used for each network. Choice of an ML algorithm should be done based on a network traffic characteristics analysis.

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