Abstract: With the development of Docker,virtualization containers of operating system level are on the rise,and contai-ner-as-a-service(CaaS) is also becoming more and more popular.With the development of container technology,the container will become the main deployment model in the cloud environment,but the integrated deployment technology for the container has not been widely studied.In the cloud environment,how to deploy a large number of containers to a suitable virtual machine to reduce the energy consumption of the data center becomes an problem which needs to to be solved urgently.Therefore,several similarity calculation methods in machine learning are used as the preference rules of the stabilization matching algorithm,and the virtual machines that have been allocated to the container are added to the preference list at the same time,which makes the one-to-one stable marriage matching algorithm update to many-to-one stable match,solving the initial deployment problem of integrate container into the virtual machine.The experimental results show that the optimal storage efficiency is about 12.8%,34.6% and 30.87% compared with the FirstFit,MostFull and Random methods respectively,and when the Euclidean distance is used as the preference rules of stabilization matching algorithm,the performance of energy saving is the best.

Key words: Containerized cloud,Container as a service,Stable matching,Container consolidation,Energy consumption