Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (6): 1714-1721.DOI: 10.11772/j.issn.1001-9081.2019101772

• Advanced computing • Previous Articles     Next Articles

Time-aware QoS prediction for SOA-based remote sensing image processing platform

XU Jinrong1, GUO Caiping1, TONG Endong2   

  1. 1. Department of Electronic Engineering, Taiyuan Institute of Technology, Taiyuan Shanxi 030008, China
    2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2019-10-18 Revised:2019-12-12 Online:2020-06-10 Published:2020-06-18
  • Contact: XU Jinrong, born in 1984, M. S., lecturer. Her research interests include image signal processing, service computing.
  • About author:GUO Caiping, born in 1981, Ph. D., associate professor. Her research interests include image processing, information inversion.XU Jinrong, born in 1984, M. S., lecturer. Her research interests include image signal processing, service computing.TONG Endong, born in 1986,Ph. D., lecturer. His research interests include service computing, artificial intelligence security.
  • Supported by:
    National Natural Science Foundation of China (61802389), the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi Province (2019L0924), the Taiyuan Institute of Technology Young and Middle-aged Leaders Project (201701).


徐金荣1, 郭彩萍1, 童恩栋2   

  1. 1.太原工业学院 电子工程系,太原 030008
    2.北京交通大学 计算机与信息技术学院,北京 100044
  • 通讯作者: 徐金荣(1984—)
  • 作者简介:徐金荣(1984—),女,福建武平人,讲师,硕士,主要研究方向:图像信号处理、服务计算.彩萍(1981—),女,山西文水人,副教授,博士,主要研究方向:图像处理、信息反演.童恩栋(1986—),男,山东聊城人,讲师,博士,主要研究方向:服务计算、人工智能安全.
  • 基金资助:

Abstract: With the help of Service-Oriented Architecture (SOA), the remote sensing image processing algorithms can be abstracted into a set of component services. Then, flexible business requirements can be met through service selection and composition. In order to get the service components that meet the user’s Quality of Service (QoS) requirements for combination, QoS of all services should be firstly obtained. Indeed, the QoS of a service is unknown to its users who have never invoked the service before. Hence, many research work have been proposed to predict the missing QoS. However, these existing methods seldom take the temporal factors into consideration, which may decrease the prediction accuracy. In order to resolve the issue, a new QoS model based on time slice was firstly proposed by considering temporal factors. Furthermore, a time-aware QoS prediction method based on Collaborative Filtering (CF) was proposed. The experiments results on the WS-DREAM real dataset show that, the proposed time-aware QoS prediction method can obtain smaller Mean Square Error (MAE) and Root Mean Square Error (RMSE).In addition, some parameters may affect the time-aware QoS prediction performance. Thus, a set of experiments and analysis with various parameter combinations were carried out, which provides a certain reference for parameter selection.

Key words: Service-Oriented Architecture (SOA), remote sensing image processing, service computing, time aware, Quality of Service (QoS) prediction

摘要: 面向服务架构(SOA)通过将遥感图像处理算法抽象成组件化的服务,进一步通过服务选择及组合,满足遥感图像处理的复杂业务需求。为了得到满足用户服务质量(QoS)要求的服务组件进行组合,前提就是获得所有服务的QoS。然而,对于用户未调用过的服务,其QoS是缺失的,因此围绕缺失QoS的预测出现了很多研究工作。针对目前QoS预测没有考虑时效性,进而影响了QoS预测准确度的问题,通过考虑时效性提出基于时间片的QoS模型,进一步基于协同过滤提出时间感知的QoS预测方法。在WS-DREAM真实数据集中的实验结果表明,时间感知的QoS预测方法能够获得较小的均方误差(MAE)和均方根误差(RMSE)。此外,对于可能影响时间感知QoS预测的多个参数,通过设置不同的参数组合进行了多次实验和分析,为参数的选择提供了一定的参考

关键词: 面向服务架构, 遥感图像处理, 服务计算, 时间感知, 服务质量预测

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