DocumentCode
258220
Title
Providing IaaS resources automatically through prediction and monitoring approaches
Author
da Silva Dias, Ariel ; Nakamura, Luis H. V. ; Estrella, Julio C. ; Santana, Regina H. C. ; Santana, Marcos J.
Author_Institution
Inst. of Math. & Comput. Sci., Univ. of Sao Paulo, São Carlos, Brazil
fYear
2014
fDate
23-26 June 2014
Firstpage
1
Lastpage
7
Abstract
A cloud computing infrastructure management is proposed in this paper, which consists of two approaches that facilitate the provisioning of computing resources in a self-adaptive virtualized environment. Resource allocation is employed to predict the future of workload management and to employ a self-adaptive approach by using computational agents to monitor the Virtual Machines (VMs). The paper also includes the Return on Investment (ROI) formula that deals with the relationship between the prices for the Infrastructure-as-a-Service (IaaS) contracted by the customer and the effective use of this service. The experimental results show a significant improvement when self-configuration is used with agent-based computational modeling in contrast with the self-configuration based on prediction for future workload.
Keywords
cloud computing; cost-benefit analysis; investment; resource allocation; virtual machines; IaaS resources; ROI formula; VMs; agent-based computational modeling; cloud computing infrastructure management; infrastructure-as-a-service; monitoring approaches; prices; resource allocation; return on investment; self-adaptive approach; self-adaptive virtualized environment; virtual machines; workload management prediction approach; Contracts; Investment; Market research; Monitoring; Servers; Time series analysis; Web services; Cloud Computing; Performance; Return on Investment; Workload Prediction;
fLanguage
English
Publisher
ieee
Conference_Titel
Computers and Communication (ISCC), 2014 IEEE Symposium on
Conference_Location
Funchal
Type
conf
DOI
10.1109/ISCC.2014.6912590
Filename
6912590
Link To Document