The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing
|
|
Author(s): Lei Li (School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China), Min Feng (21CN Co., Ltd., Guangzhou, China), Lianwen Jin (School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China), Shenjin Chen (School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China), Lihong Ma (School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China)and Jiakai Gao (Xidian University, Xi'an, China)
Copyright: 2021
Pages: 19
Source title:
Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-5339-8.ch055
Purchase
|
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.
Related Content
|
Sushruta Mishra, Sunil Kumar Mohapatra, Brojo Kishore Mishra, Soumya Sahoo.
© 2021.
24 pages.
|
|
Carlos Santos, Helena Inácio, Rui Pedro Marques.
© 2021.
16 pages.
|
|
Akash Chowdhury, Swastik Mukherjee, Sourav Banerjee.
© 2021.
26 pages.
|
|
Stojan Kitanov, Toni Janevski.
© 2021.
28 pages.
|
|
Ramesh C. Poonia, Linesh Raja.
© 2021.
27 pages.
|
|
Jens Kohler, Thomas Specht.
© 2021.
27 pages.
|
|
Jagdish Chandra Patni.
© 2021.
15 pages.
|
|
|