As the built-in scheduling strategy of Docker swarm cannot implement load balance of cluster very well and the utilization rate of cluster resource is not very high, a dynamic weighted scheduling algorithm was proposed. The weight coefficient was set on the resource, and the parameter bias was introduced to dynamically adjust the resource weight for different services. According to the actual resource utilization of each node, the node weight was calculated to reflect node load, and was used for scheduling. Compared with the original Docker scheduling strategy and the weighted scheduling strategy without parameter adjustment, the proposed algorithm makes all the resource utilization of each node in the cluster more balanced. At the same time, the proposed algorithm can achieve faster service running speed under the condition of high cluster load.

The traditional Singular Value Decomposition (SVD) recommender model based on sequential effect only considers scoring matrix and uses complicated time function to fit item's life cycle and user's preferences, which leads to many problems, such as difficult to explain model, inaccurate to capture user's preferences and low prediction accuracy. In view of the drawbacks, an improved sequential effect model was proposed which considered scoring matrix, item attributes and user rating labels comprehensively. Firstly, the time axis was divided into different phases, the project's popularity was converted to influence in[0,1] to improve project bias by sigmoid function. Secondly, the time variation changes of the user bias were transformed into time variation changes of user rating mean and overall rating mean by nonlinear function. Finally, the influence factors of the user project interaction were generated to achieve the user project interaction improvement by capturing the user's interest, combining with favorable rate of the similar users. The tests on the Movielence 10M and 20M movie scoring data sets show that the improved model can better capture the time variation change of user preferences, improve the accuracy of scoring prediction, and improve the root mean square error by 2.5%.

In the case of Channel State Information (CSI) distortion and channel fast changing, the existing clustering scheme needs to get CSI of all the base stations and generates cluster structure slowly. Concerning the problem, a fast clustering scheme based on Affinity Propagation (AP) algorithm was proposed in this paper. The scheme just needs CSI of neighboring base stations. Firstly, sparse similarity matrix was formed by the average Signal to Interfere Ratio (SIR) of cooperation between neighboring base stations. Then, among neighboring base stations, the interaction and update of collaborative information was done to quickly generate multiple clusters. Finally, the average SIR of cooperation between clusters was normal when the smaller clusters were combined to achieve the purpose of uniform cluster size. The simulation results show that the performance of the proposed scheme is better than the existing scheme in terms of convergence and cluster size uniformity.

Because of the effect of channel and noise, the effect of the traditional pilot jamming scheme will decrease because of the phase deviation. In order to avoid the problem, this paper brings forward a novel scheme based on distribution detection. Firstly, detect the phase deviation of the jamming signal by the terminal distributed in the jamming area; then the result of the detection will be sent to the jamming signal transmitter, it will drive the jamming signal phase equal to the -radian offset of the transmitted pilot tone value. Simulations prove that the scheme is effective on decreasing the phase deviation of the jamming signal.