Maintaining data freshness in distributed real-time databases

Many real-time systems need to maintain fresh views which are derived from shared data that are distributed among multiple sites. When a base data item changes, all derived views that are based on it need to be recomputed. There are two major derived data re-computation strategies - immediate update and on-demand update. However, they both have their advantages and limitations. In this paper, we study the performance of derived data update using immediate and on-demand strategies in distributed real-time databases and identify several criteria for choosing proper update policies. Based on these criteria, we propose a derived data update algorithm. In our algorithm, the update policy of a particular derived data item is determined dynamically by its access frequency, current transaction miss ratio and the system utilization. A thorough simulation study shows that our algorithm outperforms immediate and on-demand update in most cases.