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Modeling Users' Behavior for Testing the Performance of a Web Map Tile Service
Author(s) -
Guan Xuefeng,
Cheng Bo,
Song Aihong,
Wu Huayi
Publication year - 2014
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12123
Subject(s) - workload , computer science , bottleneck , service (business) , downgrade , geospatial analysis , data mining , database , real time computing , operating system , embedded system , economy , cartography , economics , geography
Abstract W eb M ap T ile S ervices ( WMTS ) are widely used in many fields to quickly and efficiently visualize geospatial data for public use. To ensure that a WMTS can successfully fulfill users' expectations and requirements, the performance of a service must be measured to track latencies and bottlenecks that may downgrade the overall quality of service ( Q o S ). Traditional synthetic workloads used to evaluate WMTS applications are usually generated by repeated static URL s, through randomized requests, or by an access log replay. These three methods do not take request characteristics and users' behaviors into consideration, while access logs are not available for systems still under development. Thus, the evaluation outcomes obtained by these methods cannot represent the real performance of online WMTS applications. In this article a new workload model named HELP ( H otspot/think‐tim E / L ength/ P ath) is proposed to measure the performance of a prototype WMTS . This model describes how users browse a WMTS map and statistically characterizes complete map navigation behaviors. Then, the HELP model is implemented in HP L oad R unner and used to generate a synthetic workload to evaluate the target WMTS . Experimental results illustrate that the performance representation of the HELP workload is more accurate than that of the other two models, and how a bottleneck in the target system was identified. Additional statistical analysis of request logs and “hotspots” visualizations further validate the proposed HELP workload.