Statistical Guarantee In QoS For Temporal Consistency Of Real-time Data Objects

The More-Less (ML) scheme has been shown to be an efficient method for maintaining temporal consistency of real-time data objects. Although ML could provide a 100% guarantee in temporal consistency, the number of update transactions that can be supported in the system is severely limited due to its use of the worst-case computation time of the jobs from update transactions in scheduling and deadline assignment. In this paper, we extend ML for the real-time database systems where the jobs from a transaction may have high variation in computation time and having a certain degree of temporal inconsistency is acceptable. We propose a new approach, called Statistical More-Less (SML), to tradeoff between quality of service (QoS) of temporal consistency and the number of update transactions that can be scheduled in the system with guaranteed deadlines. We propose an algorithm, called SML-BA, to provide the requested QoS in temporal consistency and then extend it to SML-OPT, to maximize the degree of guarantee in QoS by using the remaining processor utilization. To further improve the QoS, we extend SML-OPT by adding a slack reclaiming scheme (SML-SR) into it. The reclaimed slacks can be reallocated for processing of the jobs whose computation times are higher than the guaranteed value. Extensive performance evaluation experiments have been done to compare the SML schemes (SML-BA and SML-SR) with the deterministic schemes, More-Less and Half-Half. The performance results have shown that the SML schemes are effective schemes for trading between the schedulability of the set of transactions and the QoS guaranteed. SML-SR provides a significant improvement in QoS than that from SML-BA and SML-OPT in addition to the improvement in schedulability.