The existing event detection models rely heavily on human-annotated data， and supervised deep learning models for event detection task often suffer from over-fitting when there is only limited labeled data. Methods of replacing time-consuming human annotation data with auto-labeled data typically rely on sophisticated pre-defined rules. To address these issues， a BERT （Bidirectional Encoder Representations from Transformers） based Mix-text ADversarial training （BMAD） method for Chinese event detection was proposed. In the proposed method， a weakly supervised learning scene was set on the basis of data augmentation and adversarial learning， and a span extraction model was used to solve event detection task. Firstly， to relieve the problem of insufficient data， various data augmentation methods such as back-translation and Mix-Text were applied to augment data and create weakly supervised learning scene for event detection. And then an adversarial training mechanism was applied to learn with noise and improve the robustness of the whole model. Several experiments were conducted on commonly used real-world dataset Automatic Context Extraction （ACE） 2005. The results show that compared with algorithms such as Nugget Proposal Network （NPN）， Trigger-aware Lattice Neural Network （TLNN） and Hybrid-Character-Based Neural Network （HCBNN）， the proposed method has the F1 score improved by at least 0.84 percentage points.

To improve the resource utilization with Quality of Service (QoS) guarantee, a task and resource scheduling method under Lyapunov optimization for container cloud queue was proposed. Firstly, based on the queueing model of cloud computing, the Lyapunov function was used to analyze the variety of the task queue length. Secondly, the minimum energy consumption objective function was constructed under the task QoS guarantee. Finally, Lyapunov optimization method was used to solve the minimum cost objective function to obtain an optimization scheduling policy for the online tasks and container resources, improving the resource utilization and guaranteeing the QoS. The CloudSim simulation results show that, the proposed task and resource scheduling policy achieves high resource utilization under the QoS guarantee, which realizes the online task and resource optimization scheduling of container cloud and provides necessary reference for cloud computing task and resource overall optimization based on queuing model.

Fractal is a ubiquitous property of real-traffic, and the multi-fractal of traffic has great impact on network performances. Therefore, it is important to build the reconstructed model based on multi-fractal for the real network traffic. In this paper, a new reconstruction model based on wavelet transform that could release the correlation of traffic was build in combination with the conclusion that different traffic had different fractal characteristics in the past research, and more accuracy was got. Then, through depicting the new traffic and performance evaluation, the correctness of the new model is certificated.