Earticle

초록

영어

Service quality prediction plays a very important role in many fields related of service-oriented systems. Now, there are large numbers of approaches to predict the quality of service-oriented systems. However, it is impossible to apply one method to all prediction tasks without distinction. In reverse, unclearly to recognize the category of the prediction tasks will result in losing in all kinds of prediction approaches. In order to discover which methods are suitable for their corresponding service quality prediction scenarios, it is necessary to classify the prediction tasks and to classify the prediction approaches clearly. In this paper, a classification framework for predicting tasks for QoS of service-oriented systems is defined, four types of the prediction technologies are mainly introduced, and the research findings of quality prediction approaches for service-oriented system in literature using those four types of prediction technologies are listed and the increasing use of the method based on probabilistic model checking are specially concentrated on, and finally the result of this survey are given and the open research questions about predicting service quality by probabilistic model checking method are discussed.

목차

Abstract
 1. Introduction
 2. A Classification Framework for Predicting Tasks for QoS of Service-Oriented Systems
  2.1 From the View of the Transparency of Service Architecture
  2.2 From the View of the Measure Manner of the Service Quality Attributes
  2.3 From the View of the Feature of the Pertinent Historical Data
  2.4 From the View of the Problem Domain of Prediction Tasks
 3. Overview the Prediction Techniques
  3.1 Statistical Forecast Approaches
  3.2 Machine Learning Prediction Approaches
  3.3 Aggregation Functions Prediction Approaches
  3.4 Prediction Methods Leveraging of Formal Methods
 4. State of the Art of the Quality Prediction Approaches for Service-Oriented System
  4.1 The Applying of Statistical Prediction Methods
  4.2 The Applying of Machine Learning Prediction Methods
  4.3 The Applying of Aggregation Functions Prediction Methods
  4.4 The Applying of Formal Model Checking Prediction Methods
 5. Result of Survey
 6. Discussion
 Acknowledgement
 References

키워드

저자

• Jinyu Kai [ Shanghai University ,School of Computer Engineering and Science, Shanghai Key Laboratory of Computer Software Evaluating & Testing,Shanghai,, P.R. China ]
• Huaikou Miao [ Shanghai University ,School of Computer Engineering and Science, Shanghai Key Laboratory of Computer Software Evaluating & Testing,Shanghai,, P.R. China ]
• Honghao Gao [ Computing Center, Shanghai University, Shanghai, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605