In IaaS (Infrastructure as a Service) cloud environment, users are provisioned with virtual machines (VMs). However, the initialization and resource allocation of virtual machines are not instantaneous and usually minutes of time are needed. Therefore, to realize efficient resource provision, it is necessary to know the accurate amount of resources needed to be allocated in advance. For this purpose, this paper proposes a high-accuracy self-adaptive prediction method using optimized neural network. The characters of users demands and preferences are analyzed firstly. To deal with the specific circumstances, a dynamic self-adaptive prediction model is adopted. Some basic predictors are adopted for resource requirements prediction of simple circumstances. BP neural network with self-adjusting learning rate and momentum is adopted to optimize the prediction results. High-accuracy self-adaptive prediction is realized by using the prediction results of basic predictors with different weights as training data besides the historical data. Feedback control is introduced to improve the whole operation performance. Statistic validation of the method is conducted adopting multiple evaluation criteria. The experiment results show that the method is promising for effectively predicting resource requirements in the cloud environment.
With the gradual improvement of the telecommunication infrastructure in China, the Internet and other new technologies have been frequently used. The Internet technology also brings many network security threats, for example, botnet, while bringing convenience. Botnet is a network formed between hosts controlled by malicious code. One of the most serious threat to network security faced by the Internet is a variety of malicious network attacks on the carrier of botnet. Back propagation (BP) neural network is proposed to detect botnet threat transmission. In this study, a botnet detection model was established using BP neural network system. BP neural network classifier could identify the botnet traffic and normal traffic. Moreover a test was carried out to detect botnet traffic using BP neural network; the performance of the BP neural network classifier was evaluated by the detection rate and false positive rate. The results showed that it had high detection rate and low false positive rate, which indicated that the BP neural network had a good performance in detecting the traffic of botnet threat transmission.