Multiprocessor Scheduling of Sensor Transactions for Real-Time Data Quality Maintenance

In cyber-physical systems, sensor transactions should be effectively scheduled to maintain the temporal validity of real-time data objects. Previous studies on sensor transaction scheduling mainly focus on uniprocessor systems. In this paper, we study the problem of data quality-based scheduling of sensor transactions on multiprocessor platforms. The data quality is defined to describe the validity degree of real-time data objects. Two methods, named the Partitioned Scheduling for Quality Maximization (P-QM) and the improved P-QM scheduling (IP-QM), are proposed. P-QM maximizes the data quality by judiciously determining the preallocated computation time of each sensor transaction and assigns the transactions to different processors. IP-QM improves the data quality obtained from P-QM by adaptively executing transaction instances on each processor based on the current status of the system. It is demonstrated through experiments that IP-QM can provide higher data quality than P-QM under different system workloads.

Maintaining temporal validity of real-time data on non-continuously executing resources

This paper studies the problem of temporal validity maintenance on non-continuously-executing resources. Response time bounds for sensor transaction scheduling are derived in the context of the hybrid extended multiprocessor periodic resource model. Then two deadline and period assignment schemes are proposed to maintain the temporal validity of real-time data. The calculation based scheme (DPA-C) uses the response time bounds to compute deadlines and periods. The check based scheme (DPA-A) assigns deadlines and periods directly. It then checks the feasibility of the assignment based on the response time bounds. Experiments are conducted to evaluate the performance of the proposed schemes. The results show that DPA-C performs better than DPA-A in terms of the scheduling success ratio and the imposed update workload.