Aiming at the resource scheduling problem across data centers, a Priority Combinatorial Double Auction (PCDA) resource scheduling scheme was proposed. Firstly, cloud resource auction was divided into three parts:cloud user agent bidding, cloud resource provider bid, auction agent organization auction. Secondly, on the basis of defining user priority and task urgency, the violation of Service Level Agreement (SLA) of each job during auction was estimated and the revenue of cloud provider was calculated. At the same time, a number of transactions were allowed in each round of bidders. Finally, reasonable allocation of cloud resource scheduling according to user level could be achieved. The simulation results show that the algorithm guarantees the success rate of auction. Compared with traditional auction, PCDA reduces energy consumption by 35.00% and the profit of auction cloud provider is about 38.84%.

Due to the lack of information of Automatic Identification System (AIS) equipment, the location of a vessel cannot be accurately judged by intelligent supporting command system based on AIS. It is difficult to accurately issue the traffic signal from it. Meanwhile, due to the narrow and winding features in controlled waterway, it is difficult for traditional Kalman filter to accurately predict track of moving vessel. In this situation, the real-time estimation of system noise in Kalman filter algorithm was proposed to increase the accuracy of track prediction of moving vessel. Simulation analysis was carried out on the tracking effect of the traditional Kalman filter and improved Kalman filter. The results indicate that the proposed algorithm can solve the lack in information of AIS equipment, and accurately predict the location of a vessel. The accuracy and the reliability of intelligence supporting command system can be ensured in controlled waterway.