Abstract
The rapid growth of Internet technology makes our life more convenient and exciting. However, the number of Internet users is also increasing rapidly. More and more data requests make large-scale concurrent access to network resources. All kinds of faults appear in network services. For example, the time delay of network service and the collapse of network system are all kinds of phenomena. How to solve these problems has become a hotspot of current research. Generally speaking, the combination of neural network and cluster equalization is a more appropriate method. The combination of Neural Network and Cluster Equilibrium can make it possess some characteristics of high precision. Such as availability, feasibility, reliability and manageability. At the same time, it can improve the system throughput CPU efficiency tasks, request rate and so on. This topic is based on server cluster architecture and common classics. The classical balancing algorithm has a good effect on load balancing, but they also have some shortcomings, for example, the weight cannot be adaptively changed. For some users, the performance of request service and cluster equilibrium cannot be accurately reflected. Based on the shortcomings of existing neural networks and load balancing, an improved scheme and algorithm are proposed.
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Song, R., Huang, H. (2020). An Improved Load Balancing Algorithm Based on Neural Network. In: Xu, Z., Choo, KK., Dehghantanha, A., Parizi, R., Hammoudeh, M. (eds) Cyber Security Intelligence and Analytics. CSIA 2019. Advances in Intelligent Systems and Computing, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-030-15235-2_100
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DOI: https://doi.org/10.1007/978-3-030-15235-2_100
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