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Safety-critical embedded systems such as autonomous vehicles typically have only very limited computational capabilities on board that must be carefully managed to provide required enhanced functionalities. As these systems become more complex and inter-connected, some parts may need to be secured to prevent unauthorized access, or isolated to ensure correctness. We propose the multi-phase secure (MPS) task model as a natural extension of the widely used sporadic task model for modeling both the timing and the security (and isolation) requirements for such systems. Under MPS, task phases reflect execution using different security mechanisms which each have associated execution time costs for startup and teardown. We develop corresponding limited-preemption EDF scheduling algorithms and associated pseudo-polynomial schedulability tests for constrained-deadline MPS tasks. In doing so, we provide a correction to a long-standing schedulability condition for EDF under limited-preemption. Evaluation shows that the proposed tests are efficient to compute for bounded utilizations. We empirically demonstrate that the MPS model successfully schedules more task sets compared to non-preemptive approaches. 

Fault-tolerant coordination services have been widely used in distributed applications in cloud environments. Recent years have witnessed the emergence of time-sensitive applications deployed in edge computing environments, which introduces both challenges and opportunities for coordination services. On one hand, coordination services must recover from failures in a timely manner. On the other hand, edge computing employs local networked platforms that can be exploited to achieve timely recovery. In this work, we first identify the limitations of the leader election and recovery protocols underlying Apache ZooKeeper, the prevailing open-source coordination service. To reduce recovery latency from leader failures, we then design RT-Zookeeper with a set of novel features including a fast-convergence election protocol, a quorum channel notification mechanism, and a distributed epoch persistence protocol. We have implemented RT-Zookeeper based on ZooKeeper version 3.5.8. Empirical evaluation shows that RT-ZooKeeper achieves 91% reduction in maximum recovery latency in comparison to ZooKeeper. Furthermore, a case study demonstrates that fast failure recovery in RT-ZooKeeper can benefit a common messaging service like Kafka in terms of message latency. 

Federated scheduling is a generalization of partitioned scheduling for parallel tasks on multiprocessors, and has been shown to be a competitive scheduling approach. However, federated scheduling may waste resources due to its dedicated allocation of processors to parallel tasks. In this work we introduce a novel algorithm for scheduling parallel tasks that require more than one processor to meet their deadlines (i.e., heavy tasks). The proposed algorithm computes a deterministic schedule for each heavy task based on its internal graph structure. It efficiently exploits the processors allocated to each task and thus reduces the number of processors required by the task. Experimental evaluation shows that our new federated scheduling algorithm significantly outperforms other state-of-the-art federated-based scheduling approaches, including semi-federated scheduling and reservation-based federated scheduling, that were developed to tackle resource waste in federated scheduling, and a stretching algorithm that also uses the tasks' graph structures.