2010 · 316 citations · 11 references
ComputingCloud ComputingComputer ScienceCloud MigrationCloud Performance OptimizationVirtualizationCloud Resource ManagementCloud VirtualizationCompute Resource ManagementLoad BalancingResource SchedulingVirtual Machine MigrationCloud Load BalancingCloud SchedulingGenetic Algorithm OptimizationImbalanceMigration Cost AnalysisResource Utilization OptimizationVirtual Machine Resource AllocationVirtual Machine Scheduling
The current VM resource scheduling in cloud computing mainly considers the current system state but rarely accounts for system variation and historical data, leading to load imbalance. The paper proposes a genetic‑algorithm‑based scheduling strategy to address VM load‑balancing in cloud environments. The strategy predicts the impact of deploying VMs using historical and current system data, then selects the least‑affective configuration via a genetic algorithm to minimize load imbalance and avoid dynamic migration. Experiments show the method achieves load balancing and efficient resource utilization while reducing migration costs compared to traditional algorithms.
The current virtual machine(VM) resources scheduling in cloud computing environment mainly considers the current state of the system but seldom considers system variation and historical data, which always leads to load imbalance of the system. In view of the load balancing problem in VM resources scheduling, this paper presents a scheduling strategy on load balancing of VM resources based on genetic algorithm. According to historical data and current state of the system and through genetic algorithm, this strategy computes ahead the influence it will have on the system after the deployment of the needed VM resources and then chooses the least-affective solution, through which it achieves the best load balancing and reduces or avoids dynamic migration. This strategy solves the problem of load imbalance and high migration cost by traditional algorithms after scheduling. Experimental results prove that this method is able to realize load balancing and reasonable resources utilization both when system load is stable and variant.
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Above the Clouds: A Berkeley View of Cloud Computing
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Live migration of virtual machines
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