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

Author:

Li Lei1ORCID,Feng Min2,Jin Lianwen1,Chen Shenjin1,Ma Lihong1,Gao Jiakai3

Affiliation:

1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

2. 21CN Co., Ltd., Guangzhou, China

3. Xidian University, Xi'an, China

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.

Publisher

IGI Global

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Forecasting Resource Demand for Dynamic Datacenter Sizing in Telco Infrastructures;2023 IEEE International Conference on Big Data (BigData);2023-12-15

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