Paper Info
Title
A Probabilistic Prediction Approach for Memory Resource of Complex System Simulation in Cloud Computing Environment.
Abstract
Accurate memory resource prediction can achieve optimal performance for complex system simulation (CSS) using optimistic parallel execution in the cloud computing environment. However, because of the varying memory resource demands of CSS applications caused by the simulation entity scale and frequent optimistic synchronization, the existing approaches are unable to predict the memory resource required by a CSS application accurately, which cannot take full advantage of the elasticity and symmetry of cloud computing. In this paper, a probabilistic prediction approach based on ensemble learning, which regards the entity scale and frequent optimistic synchronization as the important features, is proposed. The approach using stacking strategy consists of a two-layer architecture. The first-layer architecture includes two kinds of base models, namely, back-propagation neural network (BPNN) and random forest (RF). The root mean squared error-based pruning algorithm is designed to choose the optimal subset of the base models. The second-layer is the Gaussian process regression (GPR) model, which is applied to quantify the uncertainty information in the probabilistic prediction for memory resources. A series of experiments are presented to prove that the proposed approach can achieve higher accuracy and performance compared to RF, BPNN, GPR, Bagging ensemble approach, and Regressive Ensemble Approach for Prediction.
Year
DOI
Venue
2020
10.3390/sym12111826
SYMMETRY-BASEL
Keywords
DocType
Volume
complex system simulation,cloud computing,probabilistic prediction,memory resource,ensemble learning
Journal
12
Issue
Citations 
PageRank 
11
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Shuai Wang100.34
Yiping Yao212031.11
Feng Zhu3116.83
Wenjie Tang44611.91
Yuhao Xiao501.01