Reservation-Based Scheduling for Automotive DSMS under High Overload Condition

Abstract

Recent automotive systems require various data, including data from on-board sensors and external sources to recognize environmental conditions. As the amount of sensor data used in automotive systems increases, processes that use such data become increasingly complicated. In addition, similar data processing can be duplicated over multiple applications. To address these issues, a data stream management system (DSMS) for automotive systems based on a data integration architecture has been developed. However, hard real-time deadlines cannot be guaranteed due to unpredictable load changes caused by data streams. For example, the arrival time and CPU utilization requested by data streams from vehicle-to-vehicle communications change rapidly depending on environmental conditions. We propose the reservation-based operator path earliest deadline first (ROP-EDF) scheduling algorithm for an automotive DSMS under overload conditions. The proposed algorithm reserves processor time preferentially for hard real-time tasks so that tasks can meet deadlines under overload conditions. ROP-EDF can be used for load testing on a single processor system. Experimental results show the effectiveness of the proposed algorithm compared with existing algorithms relative to the deadline miss ratio under overload conditions.