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In cloud data center (CDC), reducing energy consumption while maintainingperformance has always been a hot issue. In server consolidation, thetraditional solution is to divide the problem into multiple small problems suchas host overloading detection, virtual machine (VM) selection and VM placementand solve them step by step. However, the design of host overloading detectionstrategies and VM selection strategies cannot be directly linked to theultimate goal of reducing energy consumption and ensuring performance. Thispaper proposes a learning-based VM selection strategy that selects appropriateVMs for migration without direct host overloading detection. Thereby reducingthe generation of SLAV, ensuring the performance and reducing the energyconsumption of CDC. Simulations driven by real VM workload traces show that ourmethod outperforms the existing methods in reducing SLAV generation and CDCenergy consumption.

A Combination of Host Overloading Detection and Virtual Machine Selection in Cloud Server Consolidation based on Learning Method