Data Equilibrium Method of Distributed Parallel Database Under High Load

Abstract

The traditional method cannot make predictive judgment on the future load of the system, which leads to the convergence speed in the local updating process and cause the waste of resources. Aiming at this problem, a data equalization method based on ant colony optimization algorithm is proposed. During the calculation of server cluster integrated load, two kinds of load information input indicators and server indexes are mainly used. A formal description of the task scheduling problem under the high load of distributed parallel database is carried out and the mathematical model is established; the independent and different resource required virtual machine in the system are deployed in the server to balance the system, which has good global convergence, and can effectively control the system resource usage. Experiments showed that the proposed method avoids the unwanted migration caused by the instantaneous peak, which reduces the overhead of the system.