A Global Cost-Aware Container Scheduling Strategy in Cloud Data Centers

Large-scale Internet applications running on data centers are typically instantiated as a set of containers. Assigning a container to its affinity machine can reduce communication and transport costs while assigning it to the anti-affinity machine may affect the proper operation of the container. Existing container scheduling methods cannot accommodate these two types of requirements. In order to reduce the operation and maintenance cost of data centers, this article focuses on the container instance allocation problem in heterogeneous server cluster, and proposes a global cost-aware scheduling algorithm (GCCS) to solve it. The purpose is to minimize the total power consumption of the cluster from a global perspective, while trying to meet the affinity/anti-affinity requirements of applications. We study the number of containers per server selected by the application, model it as an integer linear program (ILP), and then propose a heuristic search algorithm to repair the relaxation solution of the ILP into a suboptimal feasible solution. In particular, we use Bayesian optimizer to perform a number of automated development and exploration processes for the selection of the cost coefficient. The experiments are carried out with the best cost coefficient recommended by Bayesian optimizer. Finally, the results demonstrate that GCCS can significantly reduce the total power consumption of the cluster, while maintaining a high affinity satisfaction ratio.