Paper Info
Title
PANP-GM: A Periodic Adaptive Neighbor Workload Prediction Model Based on Grey Forecasting for Cloud Resource Provisioning.
Abstract
Cloud computing platforms provide on-demand service to meet users' need by adding or removing cloud resources dynamically. The cloud resource provisioning is often based on the feedback model, which causes time delay and resource wasters. Workload prediction methods can make the resource provisioning more instantaneous and reduce resource and power consumption, to meet service level objectives (SLOs) and improve quality of service (QoS) of cloud platform. In this paper, we propose a periodic adaptive neighbor workload prediction model based on grey forecasting (PANP-GM) for cloud resource provisioning. Firstly, the model analyzes the growth rate and evaluates the periodicity of workload. Secondly, this model uses the growth rate of previous neighbor periodicity to predicate the trend of upcoming workload. To adapt to dynamic changes and emergencies, the grey forecasting model is applied for automatic error correction and improving prediction accuracy. Experimental results demonstrate that PANP-GM can achieve better resource prediction accuracy than basic and general approaches. Furthermore, this model can effectively improve the QoS of cloud platform and reduce SLO violations.
Year
DOI
Venue
2016
10.1007/978-3-319-59288-6_25
Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering
Keywords
Field
DocType
Workload prediction,Periodicity,Grey forecasting model,Resource provisioning
Service level objective,Workload,Computer science,Quality of service,Error detection and correction,Provisioning,Real-time computing,Periodic graph (geometry),Distributed computing,Cloud computing,Gray (horse)
Conference
Volume
ISSN
Citations 
201
1867-8211
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Yazhou Hu1112.59
Bo Deng213.74
Fuyang Peng370.81
Dongxia Wang4438.13
Yang Yu515138.02