Research and Prediction on Time-Sequence Characteristics of Group-User Access Behavior in Public Map Service

WU Huayi; LI Rui; ZHOU Zhen; JIANG Jie; GUI Zhipeng

doi:10.13203/j.whugis20150283

Volume 40 Issue 10

Oct. 2015

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Geomatics and Information Science of Wuhan University > 2015 > 40(10): 1279-1286,1316. > DOI: 10.13203/j.whugis20150283

WU Huayi, LI Rui, ZHOU Zhen, JIANG Jie, GUI Zhipeng. Research and Prediction on Time-Sequence Characteristics of Group-User Access Behavior in Public Map Service[J]. Geomatics and Information Science of Wuhan University, 2015, 40(10): 1279-1286,1316. DOI: 10.13203/j.whugis20150283

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Research and Prediction on Time-Sequence Characteristics of Group-User Access Behavior in Public Map Service

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2.
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China;
3.
National Geomatics Center of China, Beijing 100830, China;
4.
School of Remote Sensing and Information Science, Wuhan University, Wuhan 430079, China

Funds: The National Natural Science Foundation of China, No. 41371370; the National Key Basic Research and Development Program (973 Program) of China, No. 2012CB719906.

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Received Date: May 05, 2015
Published Date: October 04, 2015

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Abstract

Abstract

Group-user access behavior in public map service has a social nature and there is a certain group-user access pattern, which has a high access aggregative and outburst feature. However, the feature has a great influence on the demands for cloud computing resources for public map service. Thus, how to effectively express and capture the access aggregative feature and the changes of access intensity over time, and predict the access load of public map service accurately, is the important key for selecting and scheduling cloud computing resources on demand, that can address the challenge of concurrent service for massive users. Based on the volume user access logs from public map service and the time-sequence clustering method, this paper first builds a time-sequence distribution model for group-user access arriving behavior; then using the features of multi-peak, variable and periodicity in access intensity, this paper optimally partitions the time-sequence of access arrival rate in a period into different temporal patterns; as there are different probability density distribution of access arrival rate in different temporal patterns, this paper proposes a method of service load forecasting method based on a smoothing time-sequence of cumulative probability distribution. This method has a low complexity and needs few priori data. Experimental results and method application prove that the optimal partition and prediction for the access arrival rate of group-user access based on a time-sequence have a good service response performance for massive users concurrent access, improve the utilization of cloud computing resource, and balance the service quality and cost in public map service.
- public map service,
- access arrival rate,
- time series clustering,
- Fisher optimum split method,
- resource scheduling

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