With the popularization of software defined networks, switch migration as an important network management strategy has attracted increasing attention. Most existing switch migration strategies only consider local conditions and simple load thresholds, without fully considering the overall optimization and dynamics of the network. Therefore, this article proposes a switch migration algorithm based on global optimization. This algorithm adds a load prediction module to the migration model, determines the migration controller, and uses an improved whale optimization algorithm to determine the target controller and its surrounding controller set. Based on the load status of the controller and the traffic priority of the switch to be migrated, the optimal migration switch set is determined. The experimental results show that compared to existing schemes, the algorithm proposed in this paper improves the average flow processing efficiency by 15% to 40%, reduces switch migration times, and enhances the security of the controller.

To make the best use of the resources in a shared grid environment, an application scheduler must make a prediction of available performance on each resource. In this paper, we examine the problem of predicting available CPU performance in time-shared grid system. We present and evaluate a new and innovative method to predict the one-step-ahead CPU load in a grid. Our prediction strategy forecasts the future CPU load based on the variety tendency in several past steps and in previous similar patterns, and uses a polynomial fitting method. Our experimental results on large load traces collected from four different kinds of machines demonstrate that this new prediction strategy achieves average prediction errors which are between 22% and 86% less than those incurred by four previous methods.