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WSP.html
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<!DOCTYPE html>
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<title>[ICWS'12] WSP: A Network Coordinate based Web Service Positioning Framework for Response Time Prediction</title>
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<h1>WSP: A Network Coordinate based Web Service Positioning Framework<br>for Response Time Prediction</h1>
<h2 class="subheading"></h2>
<span class="meta"><a href="http://jiemingzhu.github.io" target="_blank">Jieming Zhu</a>, <a href="https://wiki.cse.cuhk.edu.hk/user/ykang/" target="_blank">Yu Kang</a>, <a href="https://wiki.cse.cuhk.edu.hk/user/zbzheng/" target="_blank">Zibin Zheng</a>, and <a href="http://www.cse.cuhk.edu.hk/lyu/" target="_blank">Michael R. Lyu</a><br><br>Department of Computer Science and Engineering<br>The Chinese University of Hong Kong</span>
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<p>With the rapid growth of Web services in recent
years, the optimal service selection from functionally-equivalent
service candidates has become more critical for building high
quality service-oriented systems. To provide accurate QoS
values for service selection, user-side QoS prediction thus
becomes an important research problem. Although collaborative filtering based prediction approaches have been studied in several previous works, these methods suffer from the
limitation of the sparsity of available historical QoS data,
which greatly degrades the prediction accuracy. To address this
problem, this paper proposes a Web service positioning (WSP)
framework for response time prediction, which is one of the
most important QoS properties. In our approach, a small set of
landmarks are deployed to periodically monitor the response
times of the Web service candidates and provide references
to the numerous service users. By combining the advantages
of network coordinate based approaches and collaborative
filtering based approaches, the response times between users
and Web services can be accurately predicted using their
corresponding Euclidean distances. Extensive experiments are
conducted based on our real-world QoS dataset collected on
PlanetLab, comprising about 359,400 response time values
from 200 users on 1,597 Web services. The experimental results
show that our WSP approach outperforms the other existing
approaches, especially when the historical data is sparse.</p>
<p>Read more from our paper: <br>
-------------------------------<br>
Jieming Zhu, Yu Kang, Zibin Zheng, and Michael R. Lyu, "WSP: A Network Coordinate based Web Service Positioning Framework for Response Time Prediction," in <i>Proc. of IEEE International Conference on Web Services (<strong>ICWS</strong>)</i>, 2012.
[<a href="http://jiemingzhu.github.io/pub/jmzhu_icws2012.pdf" target="_blank">Paper</a>][<a href="http://jiemingzhu.github.io/pub/jmzhu_icws2012_slides.pptx" target="_blank">Slides</a>]</p>
<h2 class="section-heading">Dataset Release</h2>
<p>This dataset is collected via the PlanetLab platform on Dec. 5 in 2011, comprising the RTTs between 200 distributed PlanetLab nodes and 1,597 Web services and also the RTTs between the 200 Planetlab nodes. The unit of each value is millisecond. Please refer to our paper for more information on the dataset collection.</p>
<a href="http://wsdream.github.io/dataset/icws2012_dataset" target="_blank" class="button">Download dataset</a>
List of contents of the dataset<br>
-----------------------------------<br>
1. "P2P" (298 KB): This file is a 200-by-200 data matrix, comprising RTTs between 200 PlanetLab nodes. This matrix is asymmetric.<br>
2. "W2P" (2.36 M): This file is a 1597-by-200 data matrix, comprising RTTs between 200 nodes and 1,597 Web service hosts.<br>
3. "node_list.txt" (6 KB): This file decribes the ID and address of each PlanetLab node.<br>
4. "WS_list.txt" (39 KB): This file decribes the ID and address of each Web service.<br>
5. "readme.txt" (2 KB): This file decribes the dataset in detail.
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