Data Redundancy Dynamic Control Method for High Availability Distributed Clusters

Abstract

For session control servers of carriers networks, the scale out type session control server architecture that could control system performance flexibly has been studied. Network anomaly detection technology using autoencoder has attracted attention. An autoencoder is one of the dimensionality reduction algorithm using neural network. We propose methods to prevent data loss when serious trouble occurred in network equipment, such as servers and routers, of a high availability distributed cluster using consistent hashing. The methods control data redundancy before serious failure of servers or networks occur using anomaly detection technology. We evaluated three anomalous server selection methods by calculation and computer simulation. We also verified the operation of the data redundancy dynamic control methods by software implementation and operation experiment.