Distributed QoS Evaluation for Real- World Web Services Zibin Zheng, Yilei Zhang, and Michael R. Lyu...

Distributed QoS Evaluation for Real-World Web Services

Zibin Zheng, Yilei Zhang, and Michael R. Lyulyu@cse.cuhk.edu.hk

July 07, 2010

Department of Computer Science & EngineeringThe Chinese University of Hong Kong

Hong Kong, China

ICWS 2010, Miami, Florida, USAJuly 05 - 10, 2010

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Outlines

• Introduction

• Distributed Evaluation of Web Services

• How to Use the Datasets

• Conclusion and Future Work

1. Introduction

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IntroductionWeb applications are becoming more and more important!

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Introduction• The age of Web 2.0

– Web pages and Web services

• Web services are Web APIs that can be accessed over a network and executed on remote systems.– Open standards– Interoperability

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Quality-of-Service

• Quality-of-Service (QoS): Non-functional performance. – User-independent QoS properties.

• price, popularity.• No need for evaluation

– User-dependent QoS properties. • failure probability, response time, throughput.• Different users receive different performance.

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QoS-Driven Approaches

• Web service selection

• Web service composition

• Fault tolerant Web services

• Web service ranking

• Web service recommendation

Limited real-world Web service QoS datasets for experimental studies

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Real-world Web Service Evaluation

• Obtain 21,358 publicly available Web services from the Internet.

• WS invocation and evaluation:– 235,262,555 lines of Java codes.

• Two large-scale distributed evaluations are conducted and first hand experiences are provided.– Dataset 1: 150 users * 100 Web services– Dataset 2: 339 users * 5825 Web services

2. Distributed Evaluation of Web services

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Location Information

• 21,358 Web services from 89 countries. • The top 3 countries provide 55.5% of the obtained Web s

ervices.– United States: 8867 Web services, – United Kingdom: 1657 Web services, – Germany: 1246 Web services

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Obtaining Web Service Address

• Web service portals or directories – xmethods.net, – webservicex.net,

– webservicelist.com,

• Web service searching engines– seekda.com, – esynaps.com,

• Obtain 21,358 addresses of WSDL files

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WSDL File Infomation

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Java Code Generation

• Axis 2 to generate Java codes for 13,108 Web services. • Totally 235,262,555 lines of Java codes are produced.

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Dataset 1: Failure Probability

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Overall Performance

• Mean of failure probability

• Standard deviation of failure probability

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Overall Performance

• Average failure probabilities of all of the 100 Web

services and all the 150 service users are larger th

an 0.

• The standard deviation first becomes larger with th

e increase of mean and begins to decrease after a

certain threshold.

• The standard deviations are large, indicating that p

erformance of the same Web service observed by

different service users can vary widely.

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Failure Types(1) Web service invocations can fail easily.

(2) Providing reliable Web services is not enough for building reliable service-oriented system.

(3) The Web service invocation failures are unavoidable in the unpredictable Internet environment; therefore, service fault tolerance approaches are becoming important.

(4) Service fault tolerance mechanisms should be developed at the client-side.

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Dataset 2: Response-time & Throughput

• Each service user makes one invocation on each Web services.

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Overall Response-Time• Web services with poor average response time performance tend to have la

rge performance variance to different users.

• Large response time of a Web service can be caused by the long data transferring time or the long request processing time at the server-side.

• Influenced by the client-side network conditions, different service users observe quite different average response time performance on the same Web services.

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Overall Throughput

• Influenced by the poor server-side network conditions, there is a small part of Web services providing very poor average throughput performance (<1 kbps).

• Service users with large average throughput values are more likely to observe large throughput performance variance on different Web services.

3. How to Use the Datasets?

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Web Service Recommendation

Zibin Zheng, Hao Ma, Michael R. Lyu, Irwin King, “WSRec: A Collaborative Filtering based Web Service Recommender System”, ICWS2009.

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Fault Tolerant Web Services

Zibin Zheng and Michael Lyu, “A QoS-Aware Fault Tolerant Middleware for Dependable Service Composition ”, DSN2009.

Global constraint: Success-rate of the whole service plan > 99%.

Stateless Web services

Stateful Web services

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More Research on the Datasets

• Web service selection.

• Web service search.

• Web service ranking.

• Other QoS-driven approaches of Web services.

4. Conclusion and Future Work

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Conclusion and Future Work

ConclusionDistributed evaluation of Web servicesDataset 1: 150 * 100, failure probabilityDataset 2: 339 * 5825, response-time and throughput

Future work Investigating more QoS properties Incentive mechanisms for collecting user dataEvaluating more Web services from more locations

Thank you!

Web service QoS datasets: http://www.wsdream.net