Grid Computing, IEEE/ACM International Workshop on (2011)
Sept. 21, 2011 to Sept. 23, 2011
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/Grid.2011.15
This paper considers the scenario where multiple clusters of Virtual Machines (i.e., termed as 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 being "mouldable" 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 mouldable 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 at the low transition overhead. Experiments have been conducted in this paper to evaluate the performance of the GA and the reconfiguration algorithm.
virtualization, Cluster, Cloud
Ligang He, Hai Jin, Kai Yang, Deqing Zou, Stephen A. Jarvis, Zhang Zhang, "Optimizing Resource Consumptions in Clouds", Grid Computing, IEEE/ACM International Workshop on, vol. 00, no. , pp. 42-49, 2011, doi:10.1109/Grid.2011.15