A New Cloud Computing System Optimization Model Based on Neural Network and Random Field Theory

DOI

10.2991/icsnce-16.2016.60How to use a DOI?

Keywords

Cloud computing; Optimization model; Random field; Neural network (NN)

Abstract

In this paper, we propose a new cloud computing system optimization model based on the neural network and the random field theory. Cloud computing is committed to in a more liberal environment, sharing cloud resources in a wider range of space, for a wider users to provide the more information service and knowledge service, its ultimate purpose is to provide different customers a variety of resource shared service. Cloud computing three-tier architecture reflects the different levels of the main function and its role, is to provide all kinds of targeted cloud resources shared services. To enhance the traditional systematic structure, we combine the neural network and the random field theory for modification that achieves the satisfactory optimization.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xinxin Xie
AU  - Wenzhun Huang
AU  - Shanwen Zhang
PY  - 2016/07
DA  - 2016/07
TI  - A New Cloud Computing System Optimization Model Based on Neural Network and Random Field Theory
BT  - Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering
PB  - Atlantis Press
SP  - 305
EP  - 310
SN  - 2352-5401
UR  - https://doi.org/10.2991/icsnce-16.2016.60
DO  - 10.2991/icsnce-16.2016.60
ID  - Xie2016/07
ER  -