1- Introduction

2- Related work

3- System framework

4- System model and problem formulation

5- The heuristic deployment algorithm

6- Performance evaluation

7- Conclusion

References

Abstract

The emergence of Software Defined Networking (SDN) and Network Function Virtualization (NFV) enables flexible service provisioning and deployment. However, with the continuous expansion of network scale and sharp increasing of end users, how to flexibly provide network services across multiple SDN domains for users is becoming a critical issue. A major challenge in the multi-domain network service provisioning is the network service deployment method taking into account energy efficiency. In this paper, we study the problem of how to optimally deploy network services across multiple SDN domains with the target of saving energy while achieving the load balancing of multi-domain networks. Specifically, firstly, we propose a novel multi-domain network service deployment framework by integrating SDN architecture and NFV technology, which can intelligently deploy virtual network functions (VNFs) into multi-domain networks. Secondly, we formulate this problem as a multi-objective optimization model to achieve the minimization of energy consumption and load balancing of multi-domain networks. Furthermore, we present a heuristic network service deployment algorithm to solve it. Finally, simulation results demonstrate that the proposed heuristic service deployment algorithm is efficient and outperforms comparison algorithms in terms of energy consumption and load balancing degree.

Introduction

In recent years, a large number of middleboxes (e.g., Network Address Translator (NAT), Firewall (FW) and Intrusion Detection System (IDS)) have been developed and deployed in current networks to provide various network services. However, it is inefficient for service provisioning due to the ossified architecture of traditional network and middleboxes’s heavy dependence on specific hardware equipment [1]. Fortunately, the emergence of Network Function Virtualization (NFV) brings new opportunities to address the challenge [2,3]. It implements network functions in software rather than hardware devices, running on virtual machines hosting on commodity servers [4]. One network service in NFV can be represented as one Service Function Chain (SFC), which is composed of a series of Virtual Network Functions (VNFs) in a given order. To provide efficient service, the SFC is deployed in several different servers and traffic is steered to pass through a set of VNFs in a specific sequence. NFV supports flexible service provisioning, significantly reducing Capital Expenditures (CAPEX) and Operational Expenditures (OPEX). On the other hand, Software Defined Networking (SDN), as an emerging networking paradigm, achieves the separation of control plane and data plane, and provides logically centralized control capacity as well as powerful programmable capability [5–10].

With the rapid development of urban rail transit, huge energy consumption has attracted widespread attention of scholars. The energy consumption accounts for nearly half of the total energy consumption of the subway system. Therefore, the reduction in energy consumption of the subway makes a lot of sense. Domestic and foreign scholars have done a lot of research on the optimization of urban rail transit trains and achieved many results. Generally, research methods are divided into two categories [1]: analytical algorithms and numerical algorithms. The analytical algorithm is based on the factors of trains and lines and solves the problem of train energy saving in the form of mathematical expressions. Howlett [2] used maximum principle to search for the key operating points and then got the optimal solution of the problem. Khmelnitsky [3] used the maximum value principle to optimum the control of train with different road conditions; Liu [4] used the maximum value principle to optimize the train running curve combining the optimal control strategy with the train motion model; In order to minimize energy consumption, Liang[5] made the analysis of the optimal control under different road conditions, taking slopes and speed limits into consideration. In general, it is more likely to get the best results using analytical method, but it requires complex derivation, which appears to be the difficulty of solving the problem. On the other hand, the numerical method is also based on the train and road condition, but it is a method to complete the solution within a specified time in an iterative way. Compared with the analytical method, the numerical method is easier to deal with complex objective functions, so it has attracted the attention of many scholars. Wong [6] applied genetic algorithm to determine the number of coasting point; Jin [7] applied the neural network combined with genetic algorithm to optimize the speed curve of the train. However, the use of neural network for optimization may cause the problem of long training time and slow calculation speed; In 2016, Mohammad [8] established a multi-mass train motion model of the train and compared the optimization performance of the ant colony algorithm and the genetic algorithm. And it turned out to be better when using genetic algorithm to solve the problem of energy-saving of train; Li [9] proposed the principle of handle-level variation of the dichotomy of slopes, and used genetic algorithm to solve the relationship between train handle level and train position; Wang [10] used an adaptive genetic algorithm based on a phaseadapted adaptive strategy to search for the operating point.. However, this method has a good effect at the later stage of evolution, but is not favorable to evolution in the early stage of evolution which is more likely to fall into a local optimum during evolution. For these purposes, this paper aims at finding the optimal solution of energy-saving problem. The main organization of this paper is as follows: the motion model of the train is established and control strategies are analyzed in sectionĊ. The golden ratio genetic algorithm is introduced to solve the problem of optimal controlling of the train in section ċ. The method is verified under actual subway conditions in section Č and conclusions are shown in sectionč.