The Library
Developing resource consolidation frameworks for moldable virtual machines in clouds
Tools
Tools
Tools
He, Ligang, Zou, Deqing, Zhang, Zhang, Chen, Chao, Jin, Hai and Jarvis, Stephen A. (2014) Developing resource consolidation frameworks for moldable virtual machines in clouds. Future Generation Computer Systems, Volume 32 . pp. 69-81. doi:10.1016/j.future.2012.05.015
|
PDF
WRAP_fgcs-cloudmanagement-revised.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (665Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.future.2012.05.015
Abstract
This paper considers the scenario where multiple clusters of Virtual Machines (i.e., termed Virtual Clusters) are hosted in a Cloud system consisting of a cluster of physical nodes. Multiple Virtual Clusters (VCs) cohabit in the physical cluster, with each VC offering a particular type of service for the incoming requests. In this context, VM consolidation, which strives to use a minimal number of nodes to accommodate all VMs in the system, plays an important role in saving resource consumption. Most existing consolidation methods proposed in the literature regard VMs as “rigid” during consolidation, i.e., VMs’ resource capacities remain unchanged. In VC environments, QoS is usually delivered by a VC as a single entity. Therefore, there is no reason why VMs’ resource capacity cannot be adjusted as long as the whole VC is still able to maintain the desired QoS. Treating VMs as “moldable” during consolidation may be able to further consolidate VMs into an even fewer number of nodes. This paper investigates this issue and develops a Genetic Algorithm (GA) to consolidate moldable VMs. The GA is able to evolve an optimized system state, which represents the VM-to-node mapping and the resource capacity allocated to each VM. After the new system state is calculated by the GA, the Cloud will transit from the current system state to the new one. The transition time represents overhead and should be minimized. In this paper, a cost model is formalized to capture the transition overhead, and a reconfiguration algorithm is developed to transit the Cloud to the optimized system state with low transition overhead. Experiments have been conducted to evaluate the performance of the GA and the reconfiguration algorithm.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Cloud computing -- Genetic algorithms -- Research | ||||||||
| Journal or Publication Title: | Future Generation Computer Systems | ||||||||
| Publisher: | Elsevier Science BV | ||||||||
| ISSN: | 0167-739X | ||||||||
| Official Date: | March 2014 | ||||||||
| Dates: |
|
||||||||
| Volume: | Volume 32 | ||||||||
| Page Range: | pp. 69-81 | ||||||||
| DOI: | 10.1016/j.future.2012.05.015 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Adapted As: | |||||||||
| Embodied As: | 1 |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
