Earticle

초록

영어

Predicting quality of services (QoS) is an important need when ranking cloud services for selection. QoS values of cloud services usually depend heavily on the user’s and service’s environments. Therefore, personalized QoS value prediction for cloud services is more desirable to users. Collaborative Filtering (CF) has recently been applied to personalized QoS prediction for services on the Web. However, seldom did they take the context information of service users and services into consideration. The following paper presents a CF-based method for predicting QoS values of cloud services. The method exploits not only the QoS information of users and services, but also one type of the most important context information of users and services, i.e., their geographic locations. Experiments conducted on a real dataset show that geographic location information is indeed helpful for improving the QoS prediction performance. The experimental results also demonstrate that the proposed method is significantly better than previous methods in prediction accuracy.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Overview of the Method
 4. The QoS Prediction Algorithm
  4.1. QoS Data Smoothing
  4.2. Similarity Computation
  4.3. Similar Neighbors Selection
  4.4. QoS Prediction
 5. Evaluation
  5.1. Experiment 1
  5.2. Experiment 2
  5.3. Experiment 3
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Mingdong Tang [ School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China ]
• Wei Liang [ School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China ]
• Buqing Cao [ School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China ]
• Xiangyun Lin [ School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605