Resource multi-objective mapping algorithm based on virtualized network functions: RMMA☆
Introduction
Network virtualization can be used to visualize different software platforms in the same infrastructure, manage networking differentiation services, and facilitate different protocols across coexisting heterogeneous networks [[1], [2]]. With the explosive growth of mobile traffic, heterogeneous radio access networks present a promising paradigm to improve system coverage and capacity as well as users’ quality of experience (QoE) [[3], [4]]; however, efficient resource use depends on the unified management of heterogeneous networks [[5], [6]]. Therefore, network virtualization has become an effective tool to manage these networks [[1], [2]] and is considered the key criterion of 5G networks in super-dense scenarios [[3], [4]].
Resource mapping is an important aspect of network virtualization wherein a qualified subset is selected among virtualized network functions. This is an NP-hard problem that can be reduced to a multi-way separator problem [2]. When network traffic suffers from tidal problems, each objective optimization weight varies with traffic changes. The system should be energy efficient, and users’ QoE should be guaranteed when network traffic volume is low; however, depending on application types and user preferences, users may perceive a good QoE with the same throughput when the network traffic volume is moderate. The system's throughput should be as large as possible when network traffic volume is high. Therefore, quickly identifying an optimized subset is of paramount importance. To adapt to different network traffic demands, the radio access system requires dynamic adjustment of base station transmission power and effective allocation of radio spectrum resources so that channel interference changes with network traffic. These considerations are similarly essential when dividing subsets.
In contrast to extant literature, our study focuses on the multi-objective resource mapping problem given changes in network traffic volume. The topology is based on federal architecture, such as C-RAN or Flog computing. Each federal district has different types of networks that are managed using virtual technology. We used a resource mapping method to optimize the network system's throughput, users’ QoE, and the infrastructure's energy consumption as network traffic changed. The contributions of this paper are threefold:
- (1)
A multi-objective mathematical model of throughput, QoE, and network power consumption is formulated, which is solved by extending the dynamic differential evolutionary algorithm. The proposed approach demonstrates it is possible to leverage the dynamics and flexibility of heterogeneous radio access networks.
- (2)
Machine learning is adapted to regulate the objectives weighting coefficients; in doing so, the unilateral value of the dynamic differential evolutionary algorithm mirrors the results of single-objective optimizations.
- (3)
The algorithm is evaluated in terms of time complexity, convergence, and effectiveness via Markov analysis and a simulation experiment.
The remainder of this paper is organized as follows. Section 2 outlines related work. Section 3 provides the system model, and Section 4 presents the multiple attribute mapping mathematical model. Section 5 provides a mapping method for solving multi-objective optimization. Sections 6 and 7 present the objectives, performance metrics, and evaluation results. Section 7 concludes the paper.
Section snippets
Background of resource mapping in virtualized radio access networks
At present, the global mobile Internet and Internet of Things are undergoing a rapid evolution. The capacity of the radio access network, the number of simultaneous connections, users QoE, and the base stations energy efficiency will likely face significant challenges, which drives the development of virtualized radio access networks [[39], [40], [41]]. Researchers have attempted to apply software-defined networking (SDN) and network functions virtualization to optimize the structure of radio
System model
We consider a C-RAN, which achieves centralized control of wireless access points by a centralized baseband pool [33]. At the same time, we consider a number of service providers that share the C-RAN. The network operator owns the infrastructure and the spectrum and can lease these resources to C-RAN via resource abstraction, virtualization, slicing, isolation, software-defined radio, and other means. The C-RAN centralizes signal processing and provides coordinated radio resource and
Instantaneous throughput
f1(X) is the instantaneous throughput of the network system. When the network topology is fixed, the throughput of the network is primarily related to the signal-to-interference-plus-noise ratio (SINR). The SINR of a virtual cell is shown in (1) [25]. In area G, the supply and demand relation between virtual cells and nodes is . Sinri,j is the SINR affected by a co-channel or adjacent channel when the ith node provides resources for the jth virtual cell.
Model solution
The multi-objective optimization problem addressed in this study is a dynamic multi-objective problem (DMO) concerning network traffic. This problem is related to base stations different states at different times, which may affect the base stations power consumption and throughput, users QoE, and the weights of each objective. The main aim of the dynamic optimized algorithm is to efficiently identify the optimized solution.
In the process of dynamic optimization, the environment can remain
Time complexity evaluation
The algorithms time complexity consists of the initialization and iteration functions of population optimization, as shown in
The iteration functions are determined by monitoring environmental changes and population scale, in which O(RMMA) represents time complexity. The time complexity of population initialization, the objective function to calculate populations, and elitist saving time are all O(NP). The time complexity of a population
Simulation settings
In our simulation, a heterogeneous radio network deployment scenario was considered, where overlapping A-cells, B-cells, and C-cells coexist. All base stations of the A-cells were deployed uniformly within 1600 m × 1600 m; all base stations of the B-cells and C-cells were deployed randomly in the same area. The simulation parameters of the networks are shown in Table 1. We used four server types. Every application required a certain number of nodes to fulfill its service capacity, as shown in
Conclusion
In this paper, we proposed a method for multi-objective mapping of virtualized resources for a radio heterogeneous access network. The methods objective is to perform resource distribution in a way that rectifies the disadvantages of current radio access network systems that are static and rigid. The newly proposed method capitalizes on virtualized technology and dynamically maps cut radio baseband resources to satisfy flexibility, dynamic state, and intelligence requirements. Considering the
Sai Zou received the B.S. degree in College of computer science and technology from Hengyang Normal University, Hunan, China, in 2004. He received the M.S. degree in Software engineering from Hunan University, Hunan, China, in 2007. He is currently working toward the Ph.D. degree in communications and information system with the School of information science and engineering, Xiamen University, Xiamen, Fujian, China. He was a professor at Hengyang Normal University and Chongqing College of
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Sai Zou received the B.S. degree in College of computer science and technology from Hengyang Normal University, Hunan, China, in 2004. He received the M.S. degree in Software engineering from Hunan University, Hunan, China, in 2007. He is currently working toward the Ph.D. degree in communications and information system with the School of information science and engineering, Xiamen University, Xiamen, Fujian, China. He was a professor at Hengyang Normal University and Chongqing College of Electronic Engineering, China. His current research interests are wireless communication systems and sensor network.
Yuliang Tang received the B.S. degree from Chongqing University of Posts and Telecommunications, China, in 1987. He received the M.S. degree from Beijing University of Posts and Telecommunications, China, in 1996. He received the Ph.D. degree in Communication Engineering from Xiamen University, China, in 2009. He is currently a professor at Department of Communication Engineering, Xiamen University, China. His current research interests are wireless communication systems and vehicular Ad hoc network.
Wei Ni received the B.E. and Ph.D. degrees in Electronic Engineering from Fudan University, Shanghai, China, in 2000 and 2005, respectively. Currently he is a Senior Research Scientist in the Division of CSIRO Computational Informatics (CCI), CSIRO, Australia. Prior to this he was a Research Scientist and Deputy Project Leader at the Bell Labs R and I Center, Alcatel-Lucent (2005–2008), and a Senior Research Engineer at Devices R and D, Nokia (2008–2009). His research interests include Multiuser MIMO, Relay Mesh Networks, Radio Resource Management, and Scheduling, etc. He serves as an Editorial Board Member for Hindawi Journal of Engineering since 2012.
Ren Ping Liu received his B.E. and M.E. degrees from Beijing University of Posts and Telecommunications, China, and the Ph.D. degree from the University of Newcastle, Australia. He is currently a professor at School of Computing and Communications from Faculty of Engineering and IT/University of Technology Sydney, Australia. Prior to this he is a principal scientist of networking technology in CSIRO, Australia. His research interests include Markov chain modelling, QoS scheduling, and security analysis of communication networks. Dr Liu has also been heavily involved in and led commercial projects ranging from QoS design, TCP/IP inter-networking, security, wireless networking, to next generation network architectures. As a CSIRO consultant, he delivered networking solutions to government and industrial customers, including Optus, AARNet, Nortel, Queensland Health, CityRail, Rio Tinto, and DBCDE.
Lei Wang received the PhD degree in computer science in 2005. He is now a full professor with the Department of Computer Science and Technology at Xiangtan University, China. His current research interests include security of the Internet of Things and Bioinformatics. Address: Key Laboratory of Intelligent Computing and Information Processing (Xiangtan University), Ministry of Education, China.
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This work was supported by the National Natural Science Foundation of China (61731012, 91638204, 61640210, 61672447, 61572174), and the Science Plan Project of Chongqing College of Electronic Engineering (XJPT201707), and the fifth batch of excellent talents in universities plan of Chongqing (2017.29).