High-efficiency remote cloud data center backup with intelligent parameter adaptation

Abstract

This paper aims to realize high efficient remote cloud data center backup using HBase and Cassandra, and in order to verify the high efficiency backup they have applied Thrift Java for cloud data center to take a stress test by performing strictly data read/write and remote backup in the large amounts of data. In order to optimize traffic flow of data center backup, adaptive network-based fuzzy inference system (ANFIS) along with particle swarm optimization (PSO) has been employed to off-line tune seamless handoff and network traffic flow. Finally, in terms of the effectiveness-cost evaluation to assess the remote database backup, a cost-performance ratio has been evaluated for several benchmark databases and the proposed ones. As a result, the proposed HBase approach outperforms the other databases.