2014 · 19 citations · 11 references
ComputingCloud ComputingComputer SystemsCloud InfrastructureCloud Performance OptimizationVirtualizationCloud Resource ManagementCompute Resource ManagementData Center VirtualizationEnergy-efficient Cloud ComputingResource SchedulingVirtual Machine MigrationEnergy ConsumptionHeuristic AlgorithmsObjective Function FormulationPower ConsumptionPower Consumption OptimizationResource Utilization OptimizationVirtual Machine PlacementVirtual Machines
Efficiently managing virtual machines (VMs) plays a key role in improving the resource usage and power consumption of datacenters. However, most of the existing techniques have been optimized for each system criterion separately (e.g., either platform layer or virtualization layer). Such approaches are simple to implement, but they usually result in poor system performance or under-utilization of the resources. In this paper, we investigate the problem of optimal VM management (i.e., placement and migration) in datacenters via a multi-objective function. We show that optimizing the proposed multi-objective function reduces not only the energy consumption, but also the cross network traffic among platforms. Due to the high complexity of the combinatorial optimization problem, we propose an efficient heuristic algorithm to find a near optimal solution based on the relaxed convex optimization framework. We provide both theoretical analysis and simulations to show the effectiveness of the proposed approach over the existing approaches.
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Dominant resource fairness: fair allocation of multiple resource types
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