Maintaining data temporal consistency in distributed real-time systems

Previous works on maintaining temporal consistency of real-time data objects mainly focuses on real-time database systems in which the transmission delays (jitters) of update jobs are simply ignored. However, this assumption does not hold in distributed real-time systems where the jitters of the update jobs can be large and change unpredictably with time. In this paper, we examine the design problems when the More-Less (ML) approach (Xiong and Ramamritham in Proc. of the IEEE real-time systems symposium 1999; IEEE Trans Comput 53:567–583, 2004), known to be an efficient scheme for maintaining temporal consistency of real-time data objects, is applied in a distributed real-time system environment. We propose two new extensions based on ML, called Jitter-based More-Less (JB-ML) and Statistical Jitter-based More-Less (SJB-ML) to address the jitter problems. JB-ML assumes that in the system the jitter is a constant for each update task, and it provides a deterministic guarantee in temporal consistency of the real-time data objects. SJB-ML further relaxes this restriction and provides a statistical guarantee based on the given QoS requirements of the real-time data objects. We demonstrate through extensive simulation experiments that both JB-ML and SJB-ML are effective approaches and they significantly outperform ML in terms of improving schedulability.