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Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing

Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing

Lei Li, Min Feng, Lianwen Jin, Shenjin Chen, Lihong Ma, Jiakai Gao
Copyright: © 2018 |Volume: 11 |Issue: 4 |Pages: 18
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522543237|DOI: 10.4018/JITR.2018100109
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MLA

Li, Lei, et al. "Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing." JITR vol.11, no.4 2018: pp.137-154. http://doi.org/10.4018/JITR.2018100109

APA

Li, L., Feng, M., Jin, L., Chen, S., Ma, L., & Gao, J. (2018). Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing. Journal of Information Technology Research (JITR), 11(4), 137-154. http://doi.org/10.4018/JITR.2018100109

Chicago

Li, Lei, et al. "Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing," Journal of Information Technology Research (JITR) 11, no.4: 137-154. http://doi.org/10.4018/JITR.2018100109

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Abstract

Online services are now commonly deployed via cloud computing based on Infrastructure as a Service (IaaS) to Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). However, workload is not constant over time, so guaranteeing the quality of service (QoS) and resource cost-effectiveness, which is determined by on-demand workload resource requirements, is a challenging issue. In this article, the authors propose a neural network-based-method termed domain knowledge embedding regularization neural networks (DKRNN) for large-scale workload prediction. Based on analyzing the statistical properties of a real large-scale workload, domain knowledge, which provides extended information about workload changes, is embedded into artificial neural networks (ANN) for linear regression to improve prediction accuracy. Furthermore, the regularization with noisy is combined to improve the generalization ability of artificial neural networks. The experiments demonstrate that the model can achieve more accuracy of workload prediction, provide more adaptive resource for higher resource cost effectiveness and have less impact on the QoS.

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