Dynamic function composition for network service chain: Model and optimization

Wang, Peng; Lan, Julong; Zhang, Xiaohui; Hu, Yuxiang; Chen, Shuqiao

doi:10.1016/j.comnet.2015.07.020

Cited by 46 publications

(31 citation statements)

References 22 publications

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“…We mathematically formulate this problem as a multiobjective optimization model with two different goals of minimizing total resource consumption cost and operating cost, respectively. Similar to most existing works, [10][11][12][13][14]16,[19][20][21][22][23][24][25][26][31][32][33] we transform the multiobjective optimization problem into a single objective optimization problem and base on Dijkstra algorithm to design a heuristic service deployment approach to solve it.…”

Section: Related Workmentioning

confidence: 99%

“…The static proposals may lead to network performance degradation. 17,18 Although a large number of research works [19][20][21][22][23][24][25][26][27][28][29][30] have tried to tackle the problem of dynamic network SFCs deployment, they did not consider the dynamic network service chains deployment across multiple SDN domains. Thus, an efficient dynamic cross-domain SDN network service deployment scheme is urgently required to adapt to dynamic service demands from end users.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic network service deployment across multiple SDN domains

Zhang

Wang

Dong

et al. 2019

Trans Emerging Tel Tech

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The integration of software defined networking (SDN) and network function virtualization (NFV) is a promising approach to support flexible service deployment and provisioning. In particular, NFV enables a network service to be represented as one network service function chain, which is composed of several virtual network functions in a specific order. However, with regard to service provisioning in multidomain SDN networks, virtual network functions are deployed in several different SDN domains due to limited resource capacity of each SDN domain. It results in the network service deployment complexity increase and causes additional operating cost. On the other hand, most current network service deployment proposals are limited to static user request scenarios. How to effectively deploy network service across multiple SDN domains to satisfy dynamic service demands of end users is a challenging problem. To address the aforementioned challenge, in this paper, we study the problem of dynamic network service deployment across multiple SDN domains. Specifically, we first design a novel service deployment framework to support dynamic deployment and optimization of virtual network function in multidomain SDN networks. Then, we formulate the problem as a multiobjective optimization problem with the target of jointly minimizing resource consumption cost and operating cost. Furthermore, we transform the multiobjective optimization problem into a single objective optimization model and present an efficient heuristic service deployment approach to solve it by leveraging Dijkstra algorithm. The simulation results demonstrate that the proposed service deployment method is efficient and outperforms comparison methods in terms of service rejection ratio, resource cost, and operating cost.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic network service deployment across multiple SDN domains

Zhang

Wang

Dong

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Virtual machine placement and service placement have been studied in the literature as well. For example, works presented in [29][30][31][32][33][34][35][36][37][38][39][40]. A multi-cloud virtual function distributed strategies for dynamic NFV platform has been proposed in our previous work [15].…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Efficient virtual network function placement strategies for Cloud Radio Access Networks

Bhamare

Erbad

Jain

et al. 2018

Computer Communications

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The new generation of 5G mobile services place stringent requirements for cellular network operators in terms of latency and costs. The latest trend in radio access networks (RANs) is to pool the baseband units (BBUs) of multiple radio base stations and to install them in a centralized infrastructure, such as a cloud, for statistical multiplexing gains. The technology is known as Cloud Radio Access Network (CRAN). Since cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in the telecommunication industry, CRAN is being heralded as a candidate technology to meet the expectations of radio access networks for 5G. In CRANs, low energy base stations (BSs) are deployed over a small geographical location and are connected to a cloud via finite capacity backhaul links. Baseband processing unit (BBU) functions are implemented on the virtual machines (VMs) in the cloud over commodity hardware. Such functions, built in software, are termed as virtual functions (VFs). The optimized placement of VFs is necessary to reduce the total delays and minimize the overall costs to operate CRANs. Our study considers the problem of optimal VF placement over distributed virtual resources spread across multiple clouds, creating a centralized BBU cloud. We propose a combinatorial optimization model and the use of two heuristic approaches, which are, branch-and-bound (BnB) and simulated annealing (SA) for the proposed optimal placement. In addition, we propose enhancements to the standard BnB heuristic and compare the results with standard BnB and SA approaches. The proposed enhancements improve the quality of the solution in terms of latency and cost as well as reduce the execution complexity significantly. We also determine the optimal number of clouds, which need to be deployed so that the total links delays, as well as the service migration delays, are minimized, while the total cloud deployment cost is within the acceptable limits.

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“…[12][13][14][15][16][17][18][19][20][21][22][23][24][25] In Wang et al, 12 a model on dynamic function composition problem for NSC was proposed to adapt to the increasingly diverse service requirements. [12][13][14][15][16][17][18][19][20][21][22][23][24][25] In Wang et al, 12 a model on dynamic function composition problem for NSC was proposed to adapt to the increasingly diverse service requirements.…”

Section: Related Workmentioning

confidence: 99%

“…In the past few years, the network service function chain deployment has been a hot topic in industry and academia, and a large number of research works have been carried out. [12][13][14][15][16][17][18][19][20][21][22][23][24][25] In Wang et al, 12 a model on dynamic function composition problem for NSC was proposed to adapt to the increasingly diverse service requirements. The dynamic service function composition problem was defined as a multidimension multichoice problem and solved by using the Markov approximation algorithm.…”

Section: Related Workmentioning

confidence: 99%

Network service chains deployment across multiple SDN domains

Zhang

Wang

et al. 2018

Int J Communication

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The integration of software-defined networking (SDN) and network function virtualization (NFV) enables the flexible deployment of network services and significant reduction in service provisioning cost. In particular, SDN, as a new networking paradigm, supports the flexible provisioning of new network services. Meanwhile, in NFV, the service request from end user can be represented as the service function chain (SFC). However, with the continuously increasing of network users and constant enlargement in network scale, the deployment of network services in the large-scale SDN network, which is composed of multiple SDN domains, becomes more challenging since these SDN domains are managed and maintained by different network operators and service providers. To address the above challenge, in this paper, we study the problem of how to deploy network services across multiple SDN domains optimally. Specifically, we first formulate this problem as an optimization problem by jointly minimizing the total number of SDN domains occupied by SFCs and the corresponding network resource consumption cost. We propose a novel heuristic deployment algorithm to solve this optimization problem. The simulation results demonstrate that the proposed heuristic deployment algorithm is effective and outperforms the comparison algorithms in terms of the total service cost. KEYWORDS multidomain network, network service deployment, SDN, SFC Int J Commun Syst. 2018;31:e3826.wileyonlinelibrary.com/journal/dac

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Dynamic function composition for network service chain: Model and optimization

Cited by 46 publications

References 22 publications

Dynamic network service deployment across multiple SDN domains

Dynamic network service deployment across multiple SDN domains

Efficient virtual network function placement strategies for Cloud Radio Access Networks

Network service chains deployment across multiple SDN domains

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