关键词: 大规模服务计算, 在线学习, 小批量在线学习, 随机梯度下降法

Abstract: To improve the quality of cloud services, service performance violations need to be proactively prevented instead?of?recovery triggered by the occurrence?of?failures. The performance predicting model is a promising tool for evaluating the status of Web services in cloud computing. However, traditional batch machine learning techniques could not satisfy the requirement of real-time predicting in large-scale service-oriented applications. To deal with the challenges, this paper proposes a mini-batch online learning approach to predict the performance of large-scale services. Through properly setting the batch parameters, the proposed approach uses a small fixed size of samples to train the prediction model in each iteration. This strategy efficiently reduces the computations per iteration. Furthermore, detailed theoretical analysis is conducted for online learning models and their algorithm convergence. The experimental results indicate that the proposed approach is feasible and effective.

Key words: large-scale service computing, online learning, mini-batch online learning, stochastic gradient descent