A Genetic-Based Load Balancing Algorithm in OpenFlow Network

Chou, Li-Der; Yang, Yuxiang; Hong, Yuan; Hu, Jhih Kai; Jean, Bill

doi:10.1007/978-94-007-7262-5_48

Cited by 34 publications

(23 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…19,20 The SDN handles the flows according to several characteristics of packet headers using a communication protocol such as the OpenFlow protocol. 19,20 The SDN handles the flows according to several characteristics of packet headers using a communication protocol such as the OpenFlow protocol.…”

Section: Sdn Architecture and Openflow Protocolmentioning

confidence: 99%

“…This metric is be measured by multiple indexes such as standard deviation of the load, 42 the arithmetic average for the coefficient of variation, 20 and Jain's fairness index and 38,43 entropy-based index. It should be maximized in load balancing.…”

Section: Parameters Of Load Balancingmentioning

confidence: 99%

“…Chou, Yang 20 have used the GA to provide an OpenFlow-based load balancing system. Based on the GA, the data can be efficiently distributed between clients and various servers by this system.…”

Section: Overview Of the Selected Mechanismmentioning

confidence: 99%

“…In a traditional network, flows are particularly processed according to either one or some combinations of packet header attributes, including destination Media Access Control addresses, an integration of Internet Protocol (IP) addresses and Transmission Control Protocol/User Datagram Protocol port numbers, the longest destination IP prefixes, and so on. 19,20 The SDN handles the flows according to several characteristics of packet headers using a communication protocol such as the OpenFlow protocol. [20][21][22][23][24] As illustrated in Figure 1, Open Networking Foundation divides SDN architecture into three major planes:…”

Section: Sdn Architecture and Openflow Protocolmentioning

confidence: 99%

See 3 more Smart Citations

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Neghabi

Navimipour

Hosseinzadeh

et al. 2019

Int J Communication

View full text Add to dashboard Cite

The growth of the networks has difficult network management. Recently, a concept called software-defined network (SDN) has been proposed to address this issue, which makes network management more adaptable. Control and forwarding planes are separated in SDN. The control plane is a centralized logical controller that controls the network. The forwarding plane that consists of transfer devices is responsible for transmitting packets. Because the network resources are limited, optimizing the use of resources in the networks is an important issue. Load balancing improves the balanced distribution of loads across multiple resources in order to maximize the reliability and network resources efficiency. SDN controllers can create an optimal load balancing compared to traditional networks because they have a network global view. The load-balancing problem can be solved using many different natureinspired meta-heuristic techniques because it has the NP-complete nature.Hence, for solving load balancing problem in SDN, nature-inspired metaheuristic techniques are important methods. However, to the best of our knowledge, there is not a survey or systematic review on studying these matters. Accordingly, in the area of the load balancing in the SDN, this paper reviews systematically the nature-inspired meta-heuristic techniques. Also, this study demonstrates advantages and disadvantages regarded of the chosen nature-inspired meta-heuristic techniques and considers their algorithms metrics. Moreover, to apply better load balancing techniques in the future, the important challenges of these techniques have been investigated.

show abstract

Section: Sdn Architecture and Openflow Protocolmentioning

confidence: 99%

Section: Parameters Of Load Balancingmentioning

confidence: 99%

Section: Overview Of the Selected Mechanismmentioning

confidence: 99%

Section: Sdn Architecture and Openflow Protocolmentioning

confidence: 99%

See 2 more Smart Citations

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Neghabi

Navimipour

Hosseinzadeh

et al. 2019

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Bio-inspired algorithms like ACO and GA, which are often used for route optimization, can accelerate the flow optimization along with the programability of SDN, e.g., the study in [80] presents an OpenFlow-based load balancing system with the GA but it does not consider the heterogeneity and complexity of the emerging HetNets, and hence we can expect a huge performance improvement in the near future.…”

Section: B Utilizing the Intelligence Of Software-defined Network Imentioning

confidence: 99%

Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges

2015

View full text Add to dashboard Cite

Recently, mobile networking systems have been designed with more complexity of infrastructure and higher diversity of associated devices and resources, as well as more dynamical formations of networks, due to the fast development of current Internet and mobile communication industry. In such emerging mobile heterogeneous networks (HetNets), there are a large number of technical challenges focusing on the efficient organization, management, maintenance, and optimization, over the complicated system resources. In particular, HetNets have attracted great interest from academia and industry in deploying more effective solutions based on artificial intelligence (AI) techniques, e.g., machine learning, bio-inspired algorithms, fuzzy neural network, and so on, because AI techniques can naturally handle the problems of large-scale complex systems, such as HetNets towards more intelligent and automatic-evolving ones. In this paper, we discuss the state-of-the-art AI-based techniques for evolving the smarter HetNets infrastructure and systems, focusing on the research issues of self-configuration, self-healing, and self-optimization, respectively. A detailed taxonomy of the related AI-based techniques of HetNets is also shown by discussing the pros and cons for various AI-based techniques for different problems in HetNets. Opening research issues and pending challenges are concluded as well, which can provide guidelines for future research work.INDEX TERMS Artificial intelligence, genetic algorithms, ant colony optimization, self-organization networks, heterogeneous networks.

show abstract

Middlebox selection optimization via an intelligent framework in software‐defined networking

Zadkhosh

Bahramgiri

Sabaei

2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Software‐defined networking (SDN) has a vital role in network resource utilization. However, it does not provide a comprehensive view of middleboxes (MBs). In this article, we proposed an intelligent dynamic routing framework for performance optimization based on a SDN architecture with a comprehensive view of all network properties. This routing framework also uses the genetic algorithm (GA) for performance improvement. It extracts the CPU, memory, and bandwidth utilization of MBs as dynamic routing parameters. The implemented GA calculates the impact factor (IF) of these parameters to declare the impact of each parameter in network performance. The obtained results show that considering MBs status in flow forwarding improves the tested network's resource utilization by 13, 10, and 7 times compared with hop‐based shortest path first, random path selection, and Round Robin methods, respectively. The results also showed that considering IFs (IFCPU, IFRAM, and IFBW) in routing procedure would improve the network's performance. Therefore, we used the GA to calculate optimal IFs for fairness load balancing and performance optimization. The GA calculates 0.4 and 0.6, for IFCPU and IFBW, respectively. It calculates these IFs only after five iterations. It also showed that we could ignore the RAM utilization parameter in our dynamic routing as our MBs are not memory‐bounded. The simulation results declared that routing with optimal IFs not only improves the network' throughput but also improves load distribution fairness by about 25% compared with routing without the IFs.

show abstract

A Genetic-Based Load Balancing Algorithm in OpenFlow Network

Cited by 34 publications

References 3 publications

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges

Middlebox selection optimization via an intelligent framework in software‐defined networking

Contact Info

Product

Resources

About