For Quality of Service (QoS) guarantee of cloud service areas, a cloud service QoS prediction method based on time series prediction was proposed to select an appropriate cloud service which met QoS requirements of cloud user and perceive QoS violation may occur. The improved Bayesian constant mean model was used to predict QoS of cloud service accurately. In the experiment, a Hadoop system was established to simulate cloud computing and a lot of QoS data of response time and throughput were collected as predicted object. The experimental result shows that compared with Bayesian constant mean discount model and Autoregressive Integrated Moving Average (ARIMA) model, the proposed prediction method based on improved Bayesian constant mean model is one order of magnitude smaller than the previous methods in Square Sum Error (SSE), Mean Absolute Error (MAE), Mean Squared Error (MSE) and Mean Absolut Percentage Error (MAPE), so it has higher accuracy; and the comparison of prediction accuracy illustrate that the proposed method also has better fitting effect.

For the the heavy complexity of scheduling algorithm and the misallocation of assignment occurring in the cloud computing environment, a load balancing technology based on naive Bayes algorithm was proposed. This technology made use of the heartbeat mechanism to gather every node's load information comprehensively, so as to classify the load state of all nodes based on naive Bayes algorithm. Then, according to the classification, it achieved reasonable dispatch of the task and resource for each node. The results of the experiments show that, this load balancing technology improves the efficiency of the allocation of tasks and avoids the frequent migration between nodes, so that it can achieve the purpose of balancing the load rapidly and effectively.