Intelligence‐enabled approach for load balancing in software‐defined data center networks

• Published in: International journal of communication systems 2021-06, Vol.34 (9), p.n/a
• Main Authors: C, Fancy, M, Pushpalatha
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Computer centers; data center network; Data centers; Data loss; Load balancing; Machine learning; open flow; Optimization; reinforcement learning; Software; software‐defined network
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.4818

Summary Software‐defined network provides eminent solutions for many complex network management functionalities in a data center network (DCN). One of the major tasks in any network is the load balancing in the available links. Due to dynamic data traffic nature in network, it is necessary to perform deep learning approaches for the long‐term and the short‐term data. This paper proposes a splitting policy‐based RL network (SPRLN) approach, a reinforcement learning‐based proactive load balancing algorithm that avoids the poll to the controller after the switch encounters an abnormality. The proposed method has been tested with simulations and found successful in improving the overall network performance by taking appropriate action for reward maximization. The testbed environment is treated as a Q‐learning algorithm; here, the optimality is defined as the path having the least score so that the overloaded path in that particular time can be avoided. An artificial neural network is needed because the data are uncertain all the time. Thus, the proposed SPRLN method yields 30% increased throughput and with 80% reduced data loss, when compared to existing approaches. The paper presents a reinforcement learning‐based approach for defining an intelligent load balancing approach. This will improvise the software‐defined networking (SDN) controller to take intelligent long‐term decisions to maintain network stability in the software‐defined data center network. The proposed splitting policy‐based RL network (SPRLN) policy is designed to give a proactive load balancing solution. The SPRLN method yields 30% increased throughput and with 80% reduced data loss, when compared to existing approaches.