A Fair Scheduling Algorithm for Adaptive Heterogeneous Resources in Data Centers

The resource scheduling problem of data center clusters has always been a hot topic in the field of cloud computing. Existing research efforts focus on fairness, resource utilization and energy efficiency, and lack of research on heterogeneous clustering issues. To solve the problem that the traditional DRF algorithm does not consider the classification of machine performance and task type, this paper proposes a fair scheduling algorithm X-DRF that adapts to heterogeneous resources in the data center. The algorithm mainly classifies the performance of physical machines, increases the machine performance scoring factor, and increases the training and job type judgment classification of the XGBoost model. The experiments show that CPU utilization and memory usage increased by 10% and 6%, respectively. The normalized ratio is increased by about 3% compared to the original DRF system. Therefore, the presented fair scheduling algorithm for heterogeneous resources is more fair and reasonable in terms of resource allocation.