Electrical Engineering and Computer Science
With the purpose of improving the PDP (policy decision point) evaluation performance, a novel and efficient evaluation engine, namely XDNNEngine, based on neural networks and an SGDK-means (stochastic gradient descent K-means) algorithm is proposed. We divide a policy set into different clusters, distinguish different rules based on their own features and label them for the training of neural networks by using the K-means algorithm and an asynchronous SGDK-means algorithm. Then, we utilize neural networks to search for the applicable rule. A quantitative neural network is introduced to reduce a server’s computational cost. By simulating the arrival of requests, XDNNEngine is compared with the Sun PDP, XEngine and SBA-XACML. Experimental results show that 1) if the number of requests reaches 10,000, the evaluation time of XDNNEngine on the large-scale policy set with 10,000 rules is approximately 2.5 ms, and 2) in the same condition as 1), the evaluation time of XDNNEngine is reduced by 98.27%, 90.36% and 84.69%, respectively, over that of the Sun PDP, XEngine and SBA-XACML.
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Scholarly Commons Citation
Deng, F., Song, H., Yu, Z., Zhang, L., Song, X., Zhang, M., Zhang, Z., & Mei, Y. (2021). Improvement on PDP Evaluation Performance Based on Neural Networks and SGDK-means Algorithm. Soft Computing, (). https://doi.org/10.1007/s00500-021-06447-0