  In cloud data center (CDC), reducing energy consumption while maintaining
performance has always been a hot issue. In server consolidation, the
traditional solution is to divide the problem into multiple small problems such
as host overloading detection, virtual machine (VM) selection and VM placement
and solve them step by step. However, the design of host overloading detection
strategies and VM selection strategies cannot be directly linked to the
ultimate goal of reducing energy consumption and ensuring performance. This
paper proposes a learning-based VM selection strategy that selects appropriate
VMs for migration without direct host overloading detection. Thereby reducing
the generation of SLAV, ensuring the performance and reducing the energy
consumption of CDC. Simulations driven by real VM workload traces show that our
method outperforms the existing methods in reducing SLAV generation and CDC
energy consumption.
