EDF-PStream: Earliest Deadline First Scheduling of Preemptable Data Streams -- Issues Related to Automotive Applications

Automotive applications are typical cyber-physical systems, which perform real-time continuous data processing using a variety of onboard sensors and communications from outside the vehicle. However, outside-the-vehicle data transmissions often introduce significant data rate fluctuations, where arrival times can vary or may not be guaranteed. In this study, we investigate real-time data stream processing for automotive applications based on earliest deadline first (EDF) scheduling. When sensor data with an early deadline arrive late to a data stream management system (DSMS), the EDF scheduler enqueues the late data as if they had arrived earlier. As a result, data streams are preemptable, and the stream queues do not satisfy FIFO because they are out-of-order. However, existing real-time scheduling of data streams cannot handle out-of-order queues, and searching of the out-of-order queues based on EDF degrades the performance owing to frequent accessing of the queues. In this study, we present efficient EDF scheduling for the out-of-order stream queues (i.e., Preemptable data streams) in the DSMS. The main contributions of this study are: (1) a seamless definition of EDF scheduling for preemptable data streams (EDF-PStream), which is based on the definition of general data stream processing, (2) a proposal of a reasonable task design for EDF-PStream by merging operators, and (3) a runtime evaluation of EDF-PStream using automotive applications, this includes a comparison with data stream scheduling methods.