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Proceedings ArticleDOI

Collaborative reliability prediction of service-oriented systems

TLDR
A collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations, is proposed.
Abstract
Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches.

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Citations
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Journal ArticleDOI

QoS-Aware Web Service Recommendation by Collaborative Filtering

TL;DR: This paper proposes a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users, and shows that the algorithm achieves better prediction accuracy than other approaches.
Proceedings ArticleDOI

Distributed QoS Evaluation for Real-World Web Services

TL;DR: This work conducts several large-scale evaluations on real-world Web services of quality-of-Service (QoS) performance and provides reusable research datasets for promoting the research of QoS-driven Web services.
Proceedings ArticleDOI

WSPred: A Time-Aware Personalized QoS Prediction Framework for Web Services

TL;DR: This paper proposes a Web service QoS prediction framework, called WSPred, to provide time-aware personalized QoS value prediction service for different service users, which requires no additional invocation of Web services.
Proceedings ArticleDOI

An Effective Web Service Recommendation Method Based on Personalized Collaborative Filtering

TL;DR: An effective Personalized Hybrid Collaborative Filtering (PHCF) technique by integrating personalized user- based algorithm and personalized item-based algorithm is developed based on the similarity measurement model of Web services.
Journal ArticleDOI

QoS prediction for service recommendations in mobile edge computing

TL;DR: Experimental results show that the proposed service recommendation approach based on collaborative filtering and QoS prediction based on user mobility can significantly improve on the accuracy of service recommendation in mobile edge computing.
References
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Proceedings ArticleDOI

Item-based collaborative filtering recommendation algorithms

TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Posted Content

Empirical Analysis of Predictive Algorithms for Collaborative Filtering

TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.
Proceedings Article

Empirical analysis of predictive algorithms for collaborative filtering

TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.
Journal ArticleDOI

Hybrid Recommender Systems: Survey and Experiments

TL;DR: This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
Proceedings ArticleDOI

Practical Byzantine fault tolerance

TL;DR: A new replication algorithm that is able to tolerate Byzantine faults that works in asynchronous environments like the Internet and incorporates several important optimizations that improve the response time of previous algorithms by more than an order of magnitude.
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