Intelligent Replication for Distributed Active RealTime Databases Systems

Recently, the demand for real-time database is increasing. Most real-time systems are inherently distributed in nature and need to handle data in a timely fashion. Obtaining data from remote sites may take long time making the temporal data invalid. This results in a large number of tardy transactions with their catastrophic effect. Replication is one solution of this problem, as it allows transactions to access temporal data locally. This helps transactions to meet their time requirements which require predictable resource usage. To improve predictability, Distributed Active Real-time Database System (DeeDS) prototype is introduced to avoid the delay which results from disk access, network communications and distributed commit processing. DeeDS advises to use In-memory database, fully replication and local transaction committing, but full replication consumes the system resources causing a scalability problem. In this work, we introduce Intelligent Replication In DeeDS (IReIDe) as a new replication protocol that supports the replication for DeeDS and faces the scalability problem using intelligent clustering technique. The results show the ability of IReIDe to reduce the consumed system resources and maintain scalability.