Multigranular Wavelet Decomposition-Based Support Vector Regression and Moving Average Method for Service-Time Prediction on Web Map Service Platforms

Abstract

The quality-of-service for web mapping depends on accurate service-time predictions when balancing cloud computing resources to ensure improved allocation, but the service-time on a web map service platform is nonstationary, periodic, and random, thereby making the classic prediction methods ineffective. To address this problem, we analyze the positive correlation between the service-time and access loads based on queuing theory, and build a multigranular service-time series according to variations in the volume of information contained at different time granularities. Furthermore, we propose a new wavelet decomposition (WD) based support vector regression (SVR) and moving average (MA) (WD-SVR-MA) model based on service-time decomposition theory. In order to predict the service-time accurately using our method, subsequences with nonstationary long-term trends and subsequences with stationary random fluctuations are extracted from the service-time series by WD, and predicted using the combined SVR and MA model. We compared the performance of the proposed method with other six models in experiments. Thick granularity and thin granularity service-time series were extracted from the log files of the “Tianditu” web map service platform. The proposed multigranular WD-SVR-MA model provides a variety of multigranular prediction results, demonstrating the superior performance and robustness, which are suitable for different scenarios.