基于可延迟调度提升实时数据对象时序一致性服务质量算法

针对保证实时数据对象时序一致性调度算法在软实时数据库系统环境下的应用问题,提出了一种基于概率统计的可延迟优化(SDS-OPT)算法。首先,分析和比较了现有算法在可调度性、服务质量(QoS)以及工作负载方面的特征与不足,指出优化现有算法的必要性;然后,利用最速下降法提升作业的执行时间筛选基准值,进而增加实时更新事务可调度的作业数量,以确保实时数据对象的时序一致性服务质量(QoS)最大化;最后,从工作负载和服务质量两个方面对所提算法和现有算法的性能进行对比分析。仿真实验结果表明,相对于已有的针对固定优先级可延迟调度算法(DS-FP)和统计性的非确定性可延迟调度算法(DS-PS),所提算法能够保证实时数据对象的时序一致性,同时降低工作负载,服务质量提升明显。

Algorithm for lifting temporal consistency QoS improvement of real-time data objects based on deferrable scheduling

Concerning the application problem of the existing scheduling algorithms for guaranteeing the temporal consistency of real-time data objects in the soft real-time database system environment, a Statistical Deferrable Scheduling-OPTimization (SDS-OPT)algorithm was proposed. At first, the characteristics and shortcomings of the existed algorithms were analyzed and compared in terms of scheduling, Quality of Service (QoS) and workload, then the necessity of optimizing the existing algorithms was pointed out. Secondly, in order to maximize QoS of temporal consistency for real-time data objects by advancing the schedulable job quantity of real-time updating transactions, the steepest descend method was used to increase the reference value of the screening benchmark for job execution time. Finally, the proposed algorithm was compared with the existing algorithms in terms of workload and QoS. The experimental results show that, compared with the Deferrable Scheduling algorithm for Fixed Priority transactions (DS-FP) and Deferring Scheduling-Probability Statistic algorithm (DS-PS), the proposed optimization algorithm can guarantee temporal consistency of real-time data objects effectively and reduce the workload, while the QoS is improved significantly.