A service load interval prediction method for cloud-edge collaborations based on type identification and SVMs

Service load prediction is a critical basis of cloud-edge autonomous collaborations which mainly considers the rapid response of tasks and load balancing of multiple terminals. Traditional load forecasting is usually in the form of point estimation with a relatively high variance. Frequent changes in point estimation may lead to scheduling errors and waste of resources, thus is not suitable for application scenarios of cloud-edge collaborations. To solve these problems, this paper proposed a service load interval prediction method for cloud-edge collaborations based on type identification and SVMs. The main function of the proposed method is to provide the upper and lower bounds of load forecasting suitable for cloud side collaborations with stronger adaptability to load changes. It mainly includes four steps: service load type identification, load history data interval construction, parameter optimization of SVM, and load interval prediction. This paper takes three types of cloud-edge collaborative tasks in robot target tracking (visual location, target analysis, route planning) as examples, and carried out a large number of experiments to verify the effectiveness of this method. The result shows that it outperforms traditional methods in normalized interval proportion of average width and comprehensive width coverage to a great extent.