Prediction of Cloud Resources Demand Based on Hierarchical Pythagorean Fuzzy Deep Neural Network

Having stepped into the era of information explosion, storing, processing and analyzing the vast data sometimes are quite intractable problems. However, it is impossible for personal computer or devices to tackle with such heavy workloads. Then, companies that provides cloud computational services come into business. From the perspective of companies, the cost for providing fog computing services is much higher than the traditional computing services. Consequently, the price for real-time requests is more expensive than the reserved services. Aiming at minimizing the expenditures, the most important part is how many cloud services the customers should reserve in advance because different amounts they consume will yield different expenses and both of insufficient and excess consumption result in wastes. The emerging machine learning method provides a powerful tool to address such a prediction problem. In this paper, we propose a hierarchical Pythagorean fuzzy deep neural network (HPFDNN) to forecast the quantity of requisite cloud services. On account of obtaining the better interpretations of original data, beyond the employment of fuzzy logic, the neural representation is also utilized as a complementary method. The information or the knowledge acquired from fuzzy and neural perspectives are coalesced as the final transformed data to be put into the learning systems, so that the useful information concealed in the enormous contents can be effectively described. On the basis of the anticipation of the deep neural network, the consumers are able to decide the amount of cloud services to purchase. Numerical results based on the real data set from Carnegie Mellon University demonstrate that the proposed model yields the economical predictions and outperforms the prediction by the traditional deep neural network.

Automated summary

This paper introduces a novel deep learning model HPFDNN for predicting the quantity of cloud services, which combines the methods of fuzzy logic and neural networks to better interpret original data, provide useful information, and achieve economical predictions.