DocumentCode
1789330
Title
Towards multi-resource physical machine provisioning for IaaS clouds
Author
Lei Wei ; Bingsheng He ; Chuan Heng Foh
Author_Institution
Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
fYear
2014
fDate
10-14 June 2014
Firstpage
3469
Lastpage
3472
Abstract
Virtualization has been an enabling technology for IaaS (Infrastructure as a Service) Clouds. Physical machine (PM) provisioning is a key problem for IaaS cloud providers on their resource utilization and quality of service to users. Proper provisioning is able to ensure the service quality while conserving unnecessary power consumption from over-provisioned PMs. However, the effectiveness of PM provisioning in current IaaS providers such as Amazon and Rackspace is severely limited by that they offer virtual machines with proportional resource provisioning on different resource types (including CPU, memory and disk etc). Such a rigid offering cannot satisfy diversified user applications in the cloud, and can cause significant over-provision on PMs in order to satisfy users´ requirement on all resource types. This paper argues a more flexible approach that IaaS providers should offer virtual machines with flexible combinations on multiple resource types. We further formulate the problem of multiple resource virtual machine allocations for IaaS clouds, and develop analytical models to predict the suitable number of PMs while satisfying a predefined quality-of-service requirement. Experiments show that the proposed approach can significantly increase the resource utilization, with a reduction on the number of active PMs by 27% on average.
Keywords
cloud computing; quality of service; resource allocation; virtual machines; virtualisation; Amazon; IaaS clouds; PM provisioning; Rackspace; infrastructure as a service clouds; multiresource physical machine provisioning; quality of service; resource provisioning; resource utilization; resource virtual machine allocations; virtualization; Analytical models; Computational modeling; Markov processes; Prediction algorithms; Quality of service; Resource management; Virtual machining;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications (ICC), 2014 IEEE International Conference on
Conference_Location
Sydney, NSW
Type
conf
DOI
10.1109/ICC.2014.6883858
Filename
6883858
Link To Document