Toward Practical Weakly Hard Real-Time Systems: A Job-Class-Level Scheduling Approach

Recent applications of the Internet of Things and cyber-physical systems require the integration of many sensing and control tasks into resource-constrained embedded devices. Such tasks can often tolerate a bounded number of timing violations. The concept of weakly hard real-time systems can effectively improve resource efficiency without sacrificing system safety. However, the existing studies have limitations on their practical use due to the restrictions imposed on the task timing behavior, high analysis complexity, and the lack of multicore support. In this article, we propose a new job-class-level fixed-priority preemptive scheduler and its schedulability analysis framework for weakly hard real-time tasks. Our proposed scheduler employs the meet-oriented classification of jobs of a task in order to reduce the worst-case temporal interference imposed on other tasks. Under this approach, each job is associated with a "job-class" that is determined by the number of deadlines previously met (with a bounded number of consecutively missed deadlines). This approach allows decomposing the complex weakly hard schedulability problem into two subproblems that are easier to solve: 1) analyzing the response time of a job with each job-class, which can be done by an extension of the existing task-level analysis and 2) finding possible job-class patterns, which can be modeled as a simple reachability tree. We also present a semipartitioned task allocation method for multicore platforms, which enhances the schedulability of weakly hard tasks under the proposed scheduling framework. Experimental results indicate that our scheduler outperforms the prior work in terms of task schedulability and analysis time complexity. We have also implemented a prototype of a job-class-level scheduler in the Linux kernel running on Raspberry Pi with acceptably small-runtime overhead.