Task Failure Prediction in Cloud Data Centers Using Deep Learning

A large-scale cloud data center needs to provide high service reliability and availability with low failure occurrence probability. However, current large-scale cloud data centers still face high failure rates due to many reasons such as hardware and software failures, which often result in task and...

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Bibliographic Details
Published in:IEEE transactions on services computing Vol. 15; no. 3; pp. 1411 - 1422
Main Authors: Gao, Jiechao, Wang, Haoyu, Shen, Haiying
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Cloud computing
cloud data center
Computer centers
Data centers
Deep learning
Failure
Failure analysis
Failure rates
Machine learning
Multilayers
Predictive models
Reliability
Software
Support vector machines
Task analysis
Task failure
ISSN:1939-1374, 2372-0204
Online Access:Get full text
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Summary:A large-scale cloud data center needs to provide high service reliability and availability with low failure occurrence probability. However, current large-scale cloud data centers still face high failure rates due to many reasons such as hardware and software failures, which often result in task and job failures. Such failures can severely reduce the reliability of cloud services and also occupy huge amount of resources to recover the service from failures. Therefore, it is important to predict task or job failures before occurrence with high accuracy to avoid unexpected wastage. Many machine learning and deep learning based methods have been proposed for the task or job failure prediction by analyzing past system message logs and identifying the relationship between the data and the failures. In order to further improve the failure prediction accuracy of the previous machine learning and deep learning based methods, in this article, we propose a failure prediction algorithm based on multi-layer Bidirectional Long Short Term Memory (Bi-LSTM) to identify task and job failures in the cloud. The goal of Bi-LSTM failure prediction algorithm is to predict whether the tasks and jobs are failed or completed. The trace-driven experiments show that our algorithm outperforms other state-of-art prediction methods with 93 percent accuracy and 87 percent for task failure and job failures respectively.
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ISSN:1939-1374
2372-0204
DOI:10.1109/TSC.2020.2993728