Online QoS Prediction for Runtime Service Adaptation via Adaptive Matrix Factorization

Authors:
Top Cited Papers
Publisher website
Google Scholar
Abstract
Cloud applications built on service-oriented architectures generally integrate a number of component services to fulfill certain application logic. The changing cloud environment highlights the need for these applications to keep resilient against QoS variations of their component services so that end-to-end quality-of-service (QoS) can be guaranteed. Runtime service adaptation is a key technique to achieve this goal. To support timely and accurate adaptation decisions, effective and efficient QoS prediction is needed to obtain real-time QoS information of component services. However, current research has focused mostly on QoS prediction of working services that are being used by a cloud application, but little on predicting QoS values of candidate services that are equally important in determining optimal adaptation actions. In this paper, we propose an adaptive matrix factorization (namely AMF) approach to perform online QoS prediction for candidate services. AMF is inspired from the widely-used collaborative filtering techniques in recommender systems, but significantly extends the conventional matrix factorization model with new techniques of data transformation, online learning, and adaptive weights. Comprehensive experiments, as well as a case study, have been conducted based on a real-world QoS dataset of Web services (with over 40 million QoS records). The evaluation results demonstrate AMF's superiority in achieving accuracy, efficiency, and robustness, which are essential to enable optimal runtime service adaptation.
Funding Information
  • National Natural Science Foundation of China (61332010, 61472338)
  • National Basic Research Program of China 973 Project (2014CB347701)
  • Research Grants Council of the Hong Kong Special Administrative Region (CUHK 415113)
  • General Research Fund (2015)
  • Microsoft Research Asia Collaborative Research Program (FY16-RESTHEME-005)

This publication has 39 references indexed in Scilit: