Predicting of Job Failure in Compute Cloud Based on Online Extreme Learning Machine: A Comparative Study

• Published in: IEEE access 2017, Vol.5, p.9359-9368
• Main Authors: Liu, Chunhong, Han, Jingjing, Shang, Yanlei, Liu, Chuanchang, Cheng, Bo, Chen, Junliang
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Cloud cluster; Comparative studies; Computational modeling; Data centers; Data models; Google; jobs termination status; Machine learning; Model accuracy; online predicting; OS-ELM model; Predictive models; Real-time systems; Resource utilization; Support vector machines; the Google cluster traces
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2706740

Early prediction of job failures and specific disposal steps in advance could significantly improve the efficiency of resource utilization in large-scale data center. The existing machine learning-based prediction methods commonly adopt offline working pattern, which cannot be used for online prediction in practical operations, in which data arrive sequentially. To solve this problem, a new method based on online sequential extreme learning machine (OS-ELM) is proposed in this paper to predict online job termination status. With this method, real-time data are collected according to the sequence of job arriving, the job status could be predicted and the operation model is thus updated based on these data. The method with online incremental learning strategy has fast learning speed and good generalization. Comparative study using Google trace data shows that prediction accuracy of the proposed method is 93% with updating model in 0.01 s. Compared with some state-of-the-art methods, such, as support vector machine (SVM), ELM, and OS-SVM, the method developed in this paper has many advantages, such as less time-consuming in establishing and updating the model, higher prediction accuracy and precision, and better false negative performance.