Abstract
Abstract With the rapid development of cloud computing and other related services, higher requirements are put forward for network transmission and delay. Due to the inherent distributed characteristics of traditional networks, machine learning technology is difficult to be applied and deployed in network control. The emergence of SDN technology provides new opportunities and challenges for the application of machine learning technology in network management. A load balancing algorithm of Internet of things controller based on data center SDN architecture is proposed. The Bayesian network is used to predict the degree of load congestion, combining reinforcement learning algorithm to make optimal action decision, self-adjusting parameter weight to adjust the controller load congestion, to achieve load balance, improve network security and stability.
Highlights
With the rapid development of Internet technology and mobile communication technology, the infrastructure, resources and structure of network system become more and more complex
This paper proposes a self-learning load balancing algorithm for software defined networks (SDN) controller based on Bayesian network (BN) which predict the load congestion
SDN controller self-learning system based on Bayesian network, The core controller node is defined as Cj; the common switch node is defined as Si; the connection between the switch and the controller is defined as bij, with a value of 0 or 1; the total load of all switches in the topology is defined as LS; and the number of all controllers in the topology is defined as NC
Summary
With the rapid development of Internet technology and mobile communication technology, the infrastructure, resources and structure of network system become more and more complex. In multi-controller condition, load balancing of controller is serious problem which impacts the controller resource utilization rate[1−3], it is one of the important methods to ensure the quality of network service, it is mainly to expand the data processing ability of the network by reasonably scheduling the network and computing resources, so as to avoid network congestion and improve the performance and robustness of the network, based on the network security, realize the network security optimization and load balance of the controller. There are many studies focus on the reinforcement learning method in load balancing of SDN controllers, but it has not been combined with Bayesian method[23−25]. This paper proposes a self-learning load balancing algorithm for SDN controller based on Bayesian network (BN) which predict the load congestion. This algorithm selects the optimal action strategy according to the prediction result by the reinforcement learning. The simulation results indicate this proposed algorithm can improve the performance of controller load balancing in SDN, to improve network security and stability
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
