Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Combining batch execution and leasing using virtual machines
HPDC '08 Proceedings of the 17th international symposium on High performance distributed computing
Generating Adaptation Policies for Multi-tier Applications in Consolidated Server Environments
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
HPCC '08 Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications
SLA-Driven Semantically-Enhanced Dynamic Resource Allocator for Virtualized Service Providers
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
Dynamic memory balancing for virtual machines
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Entropy: a consolidation manager for clusters
Proceedings of the 2009 ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Multi-Tiered On-Demand Resource Scheduling for VM-Based Data Center
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Modeling virtual machine performance: challenges and approaches
ACM SIGMETRICS Performance Evaluation Review
Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures
ICDCS '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems
Allocating Resources for Workflows Running under Authorization Control
GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
Developing resource consolidation frameworks for moldable virtual machines in clouds
Future Generation Computer Systems
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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.