The invention discloses an adaptive configuration method for short-
time variable big data job cluster scheduling. The method is a self-adaptive cluster scheduler
configuration optimization method provided by aiming at the characteristics of cloud platform isomerism, cluster scheduler
configuration optimization of dynamic load, isomerism of cloud platform load and short-time variability. Cloud platform loads can be divided into service applications and analysis applications, and different classifications are different in
resource consumption and time requirements. According to the method, the configuration of the cluster scheduler is adjusted according to the state information of the job and the information of the cluster environment; the
optimal scheduling configuration is always kept; therefore, the operation performance is improved, the operation
delay is reduced, the heterogeneous load of the cloud platform can be better adapted, and the optimal
configuration item corresponding to the current
cluster state can be better found, so that the
waiting time of the cluster operation is close to the minimum, the operation efficiency is improved, and the short-
time variable big data operation is effectively scheduled.