Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems · 2007 · 48 citations · 34 references
ComputingComputer SystemsInformation TechnologyCloud InfrastructureResource Utilization AnalysisVirtualizationData Center VirtualizationResource ProvisioningResource SchedulingServer VirtualizationVirtual Machine MigrationVirtualization ArchitectureDynamic Resource AllocationDynamic Resource ManagementDynamic Resource ProvisioningResponsivenessRuntime ManagementRuntime Resource ManagementVirtual Machine PlacementVirtual Machines
Virtualization is increasingly popular, decoupling resources from users and offering greater flexibility, but it also introduces challenges for optimal design, provisioning, and runtime management. This study introduces an analytical model that estimates the expected response‑time improvement from virtual‑machine migration decisions. The model incorporates resource‑demand predictability, infrastructure characteristics such as migration time and overhead, and is validated through simulations based on real data‑center utilization traces. The results reveal decision strategies that enhance overall system responsiveness during dynamic virtual‑machine management.
Virtualization rapidly gains popularity affecting multiple levels of the computing stack. Since it decouples resources from their users it provides greater flexibility in terms of resource allocation but also brings new challenges for optimal design, provisioning, and runtime management of systems. This paper presents an analytical model of virtual machine migration that provides an estimate of the expected improvement in response time due to a migration decision. The model explicitly accounts for predictability of resource demands and characteristics of the virtualization infrastructure, such as migration time and overhead. It brings new insights into the decision strategies that should be used to improve the overall responsiveness of the system when dynamically managing virtual machines. The model is validated using simulations driven by data center resource utilization traces.
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