刘宵婧, 桂志鹏, 曹军, 李锐, 吴华意. GWR与STARMA结合的WMS响应时间时空预测模型[J]. 武汉大学学报 ( 信息科学版), 2018, 43(6): 951-958. DOI: 10.13203/j.whugis20160370
引用本文: 刘宵婧, 桂志鹏, 曹军, 李锐, 吴华意. GWR与STARMA结合的WMS响应时间时空预测模型[J]. 武汉大学学报 ( 信息科学版), 2018, 43(6): 951-958. DOI: 10.13203/j.whugis20160370
LIU Xiaojing, GUI Zhipeng, CAO Jun, LI Rui, WU Huayi. Spatiotemporal-Aware Hybrid Prediction Model for Response Time of Web Map Services by Integrating GWR and STARMA[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 951-958. DOI: 10.13203/j.whugis20160370
Citation: LIU Xiaojing, GUI Zhipeng, CAO Jun, LI Rui, WU Huayi. Spatiotemporal-Aware Hybrid Prediction Model for Response Time of Web Map Services by Integrating GWR and STARMA[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 951-958. DOI: 10.13203/j.whugis20160370

GWR与STARMA结合的WMS响应时间时空预测模型

Spatiotemporal-Aware Hybrid Prediction Model for Response Time of Web Map Services by Integrating GWR and STARMA

  • 摘要: 响应时间作为一项非功能性属性,是网络服务性能的重要度量指标。它直接影响了用户的服务体验,并在服务资源选择中扮演重要的角色。响应时间不仅受制于服务自身的软硬件性能,同时还受到用户访问时空分布差异性的影响,具有显著的不确定性,因此如何可靠地预测响应时间是一个难点。选取OGC WMS(web map service)为研究对象,通过全球多地分布式部署的监测系统获取服务响应时间,在分析WMS响应时间与时空因素的关联关系及其变化规律基础上,提出地理加权回归(geographical weighted regression,GWR)与时空自相关移动平均(spatial-temporal auto regressive and moving average,STARMA)相结合的WMS响应时间时空预测模型。该模型综合考虑了用户访问时空分异特征对WMS响应时间的影响,其中GWR部分描述服务响应时间的时空趋势,STARMA部分拟合时空序列局部随机扰动。通过将多个地区监测点不同时刻WMS响应时间的实测数据与模型预测值对比,验证了模型的有效性。实验表明,该模型的预测精度相比经典的平均值法AVG有较大的提升,同时较GWR模型和STARMA模型的精度有一定程度的改善。

     

    Abstract: As a non-functional attribute, response time is an important measurement of web service performance. Response time impacts user experience significantly and thus plays an important role in web service selection. However, response time has significantly uncertainty and is hard to predict because it is not only determined by software and hardware performance, but also affected by spatiotemporal distribution of users. In this paper, we take the OGC web map service (WMS) as an example to analyze the correlation between WMS response time and spatiotemporal factors. We collected the monitoring data of response times from thousands of WMSs using a globally distributed monitoring system we developed. Based on data analysis, we propose a hybrid spatiotemporal prediction model that takes the impact of both space and time disparities of user access into account, by integrating geographical weighted regression (GWR) and spatialtemporal auto regressive and moving average (STARMA). Specifically, GWR component simulates macro-level spatiotemporal trends in response time and the STARMA component captures local stochastic variations in spatiotemporal series. By comparing the predicated data with monitoring data from different monitoring sites and at different times, the feasibility was tested. The experimental results show that the proposed model delivers more significant predication accuracy improvement than the classical average model (AVG). The hybrid model also achieves a slight accuracy improvement over GWR and STARMA. We conclude with a discussion of the applicable scenarios of the models.

     

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