A High-accuracy Self-adaptive Resource Demands Predicting Method in IaaS Cloud Environment

Title:

A High-accuracy Self-adaptive Resource Demands Predicting Method in IaaS Cloud Environment

Creator:

Chen , Z., Zhu , Y., Di , Y., Feng, S., and Geng , J.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:f89c9989-8ed5-4dc0-820b-b31ba8e2ac5b
uuid:f89c9989-8ed5-4dc0-820b-b31ba8e2ac5b
doi:10.14311/NNW.2015.25.026

Subject:

IaaS cloud enviroment, user demandprediction, high-accuracy self-adaptive prediction, BP neural network, self-adjusting learning rate and momentum, and static evaluation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In IaaS (Infrastructure as a Service) cloud environment, users are provisioned with virtual machines (VMs). However, the initialization and resource allocation of virtual machines are not instantaneous and usually minutes of time are needed. Therefore, to realize efficient resource provision, it is necessary to know the accurate amount of resources needed to be allocated in advance. For this purpose, this paper proposes a high-accuracy self-adaptive prediction method using optimized neural network. The characters of users’ demands and preferences are analyzed firstly. To deal with the specific circumstances, a dynamic self-adaptive prediction model is adopted. Some basic predictors are adopted for resource requirements prediction of simple circumstances. BP neural network with self-adjusting learning rate and momentum is adopted to optimize the prediction results. High-accuracy self-adaptive prediction is realized by using the prediction results of basic predictors with different weights as training data besides the historical data. Feedback control is introduced to improve the whole operation performance. Statistic validation of the method is conducted adopting multiple evaluation criteria. The experiment results show that the method is promising for effectively predicting resource requirements in the cloud environment.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2015 Volume:25 | Number:5

Harvested from:

CDK

Metadata only:

false