Evolutionary Algorithms in Software Defined Networks: Techniques, Applications, and Issues

doi:10.3969/j.issn.1673-5188.2017.03.004

ZTE Communications ›› 2017, Vol. 15 ›› Issue (3): 20-36.DOI: 10.3969/j.issn.1673-5188.2017.03.004

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Evolutionary Algorithms in Software Defined Networks: Techniques, Applications, and Issues

LIAO Lingxia¹, Victor C. M. Leung¹, LAI Chin-Feng²

1. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver BC V6T 1Z4, Canada
2. Department of Engineering Science, National Cheng Kung University, Tainan, China

Received:2017-06-08 Online:2017-08-25 Published:2019-12-24
About author:LIAO Lingxia (liaolx@ece.ubc.ca) is currently a Ph.D. candidate of Department of Electrical and Computer Engineering of the University of British Columbia (UBC), Canada. She had a bachelor degree from Tsinghua University, China and a master degree from UBC. She was a science researcher in Computer Science Department of UBC, and the R&D director and the general manager of high performance computing of Inspur Group, China. She had over ten years’ experience working in computer and network industry. Her current research interests are E-hearthcare, cloud computing, software defined networking, network function virtualization, network monitoring and optimization, and next generation network. She has contributed multiple technical papers and book chapters in these areas.|Victor C.M. Leung (vleung@ece.ubc.ca) is a professor of Electrical and Computer Engineering and holder of the TELUS Mobility Research Chair at the University of British Columbia (UBC). He has contributed some 1000 technical papers, 37 book chapters and 12 book titles in the areas of wireless networks and mobile systems. He was a Distinguished Lecturer of the IEEE Communications Society. He is serving/has served on the editorial boards of the IEEE Journal on Selected Areas in Communications, IEEE Access, IEEE Transactions on Wireless Communications, IEEE Transactions on Computers, IEEE Transactions on Vehicular Technology, IEEE Wireless Communications Letters and several other journals, and has contributed to the organizing and technical program committees of numerous conferences. Dr. Leung was a winner of the 2011 UBC Killam Research Prize, the IEEE Vancouver Section Centennial Award, the 2017 Canadian Award for Telecommunications Research. He co-authored a paper that won the 2017 IEEE Communications Society Fred W. Ellersick Prize.|LAI Chin-Feng (cinfon@ieee.org) is an associate professor at Department of Engineering Science, National Cheng Kung University, China and Department of Computer Science and Information Engineering, National Chung Cheng University, China since 2016. He received the Ph.D. degree in Department of Engineering Science from National Cheng Kung University, China in 2008. He received Best Paper Awards from IEEE 17th CCSE, 2014 International Conference on Cloud Computing, IEEE 10th EUC, and IEEE 12th CIT. He has more than 100 paper publications and 4 papers selected to TOP 1% most cited articles by Essential Science Indicators (ESI). He serves as an associate editor-in-chief, editor, or associate editor for many journals and is TPC Co-Chair for many conferences during 2012-2017. His research focuses on Internet of Things, body sensor networks, E-healthcare, mobile cloud computing, cloud-assisted multimedia network, embedded systems, etc. He has been an IEEE senior member since 2014.

Abstract

Abstract:

A software defined networking (SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typically very complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve these problems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionary algorithms (EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviews four types of EAs that are widely applied in current SDNs: Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the techniques and categorizes the applications of these four EAs in SDNs.

Key words: SDN, evolutionary algorithms, Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Simulated Annealing

LIAO Lingxia, Victor C. M. Leung, LAI Chin-Feng. Evolutionary Algorithms in Software Defined Networks: Techniques, Applications, and Issues[J]. ZTE Communications, 2017, 15(3): 20-36.

Figures/Tables 9

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Process	Mechanisms	Descriptions
Initialization	Binary encoding Value encoding Permutation encoding Tree encoding	A solution is a bit string with each element as 0 or 1 A solution is a string with elements integers, real numbers, characters, or objects A solution is a sequence of number A chromosome is a tree form of objects
Selection	Relative tournament Routlette wheel Relative pooling tournament Elitism	Two members are randomly chosen, and the parent is the one with higher fitness The probability an individual to be chosen as a parent is depended on their fitness Populations are thrown into a competition, and the winners are the parents Populations with the higher fitness from all the populations generated so far from the parent set
Crossover	One-point Two-point Uniform Cut and spice Ordered chromosome	Randomly generate a crossover point Randomly generate two crossover points Use a fixed mixing ratio between two parents Allow each parent to have its own choice in deciding crossover point Switch the position of genes
Mutation	Bit-string Flip bit Boundary Gaussian Uniform	Randomly flip the value of genes Flip the value of selective genes Replace the value of a gene with the upper or lower bound of the value Add a unit Gaussian distributed random value to the selected chromosome Replace the value of a selective gene with a uniform random value within the user-specified bounds

Process	Mechanisms	Descriptions
Initialization	Binary encoding Value encoding Permutation encoding Tree encoding	A solution is a bit string with each element as 0 or 1 A solution is a string with elements integers, real numbers, characters, or objects A solution is a sequence of number A chromosome is a tree form of objects
Selection	Relative tournament Routlette wheel Relative pooling tournament Elitism	Two members are randomly chosen, and the parent is the one with higher fitness The probability an individual to be chosen as a parent is depended on their fitness Populations are thrown into a competition, and the winners are the parents Populations with the higher fitness from all the populations generated so far from the parent set
Crossover	One-point Two-point Uniform Cut and spice Ordered chromosome	Randomly generate a crossover point Randomly generate two crossover points Use a fixed mixing ratio between two parents Allow each parent to have its own choice in deciding crossover point Switch the position of genes
Mutation	Bit-string Flip bit Boundary Gaussian Uniform	Randomly flip the value of genes Flip the value of selective genes Replace the value of a gene with the upper or lower bound of the value Add a unit Gaussian distributed random value to the selected chromosome Replace the value of a selective gene with a uniform random value within the user-specified bounds

	GA	PSO	ACO	SA
Motivation	Natural evolution	Bird migration	Ant search food	Solid in heat bath
Population size	Any size	Several	Any size	Typically two
Iterations	Yes	Yes	Yes	Yes
Single-objective	Yes	Yes	Yes	Yes
Multi-objective	Yes	Yes	Yes	Yes
Parallelism	Inherent parallelism	Extended	Inherent parallelism	Extended
Convergence	Slow	Fast	Uncertain	Slow
Solution quality	Near global optima	Near global optima	Near global optima	Near local optima
Applications	General	General	TSP, routing, dynamic and adaptive	General
Theoretical analysis	Hard	Hard	Hard	Hard

	GA	PSO	ACO	SA
Motivation	Natural evolution	Bird migration	Ant search food	Solid in heat bath
Population size	Any size	Several	Any size	Typically two
Iterations	Yes	Yes	Yes	Yes
Single-objective	Yes	Yes	Yes	Yes
Multi-objective	Yes	Yes	Yes	Yes
Parallelism	Inherent parallelism	Extended	Inherent parallelism	Extended
Convergence	Slow	Fast	Uncertain	Slow
Solution quality	Near global optima	Near global optima	Near global optima	Near local optima
Applications	General	General	TSP, routing, dynamic and adaptive	General
Theoretical analysis	Hard	Hard	Hard	Hard

Application	Category	EA	Description
Liu in [57]	Routing	GA	Avoid link congestion
Ren in [60]		GA	Avoid switch congestion
Maniu in [62]		GA	Optimize link usage
Kikuta in [63]		Parallel GA	Optimize explicit routing in GPU
Stefano in [64]		ACO	Optimize network bandwidth usage
Wang in [65]		ACO	Avoid link congestion
Zhu in [67]		PSO	Energy saving
Subbiah in [68]		PSO	Finding the best node connector in switches for energy saving
Awad in [69]		PSO	Energy saving under the constraint of size-limited flow table
Dobrijevic in [70]		ACO	QoE aware routing
Tang in [71]		ACO	QoE aware routing
Blaguer in [72]		GA	QoE aware routing
Santl M in[73]		ACO	QoE aware routing
Kang in [74]	Load balancing	GA	Controller load balancing
Chou in [75]		GA	Controller load balancing
AMR in [77]		GA	ink load balancing
Sathyanarayana in [76]		ACO	Controller and link load balancing
Lin in [72]		ACO	Controller load balancing
Lange in [79]	Controller placement	SA	Minimize swi-to-con delay and controller load imbalance
Sanner in [80]		GA	Minimize swit-to-con delay
Jalili in [81]		GA	Minimize con-to-con delay and controller load imbalance
Ahmadi in [82]		GA	Minimize swi-to-con delay, con-to-con delay, controller load imbalance
Gao in [83]		PSO	Minimize swi-to-con delay considering controller capacity
Liu in [84]		PSO	Minimize swi-to-con delay and controller load imbalance
Li in [86]	Security	GA	Detect DDos attacks
Li in [87]		GA,PSO	Detect DDos attacks
Chen in [88]		ACO	Detect DDos attacks
Liu in [89]		ACO	Detect DDos attacks
Ojugo in [90]		GA	Security rule generation
Zhao in [91]		GA	Intrusion action detecting
Bouet in [92]		GA	Single security appliance placement
Famaluddine in [93]		GA	Multiple security appliances placement
Li in [94]	Virtual network mapping	PSO	Optimize network resource usage
Yao in [95]	Flow table optimization	PSO	Optimize flow table usage
Gao in [96]		ACO	Optimize flow table usage
Li in [97]		ACO	Optimize flow table usage
Guo in [98]	Hybrid SDN migration	GA	Migrate routers in hybrid network

Evolutionary Algorithms in Software Defined Networks: Techniques, Applications, and Issues

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