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To certify the schedulability of a system, valid per-task worst-case execution-time (WCET) estimates are almost always required. Unfortunately, on multicore machines, deriving WCET estimates through static analysis that is not highly pessimistic may never be a practical reality. The alternative is to determine WCETs via a measurement process, but such a process cannot correctly produce accurate WCET estimates with certainty. This lack of certainty necessitates the use of overrun-handling mechanisms, such as budget-enforcement techniques, to preserve temporal correctness at runtime. In many systems of interest today, tasks are interconnected to form processing graphs, which can be quite large. The simplest (and perhaps most common) approach to budget enforcement in this case is to abort an entire graph invocation whenever any node (task) overruns its budget. However, such an approach can result in a high abort rate at the graph level even when the per-node abort rate is low. To remedy this situation, this paper presents a holistic budget-management strategy for directed acyclic graphs (DAGs) that involves reallocating per-node budgets to overrunning nodes to avoid DAG-level aborts. To enable the effects of aborts to be studied analytically, a probabilistic analysis is presented to derive a DAG’s abort rate under the proposed budget-management strategy. Experimental results are also presented to demonstrate the utility of budgeting graphs holistically 

This paper presents a real-time locking protocol whose design was motivated by the goal of enabling safe GPU sharing in time-sliced component-based systems. This locking protocol enables a GPU to be shared concurrently across, and utilized within, isolated components with predictable execution times. It relies on a novel resizing technique where GPU work is dimensioned on-the-fly to run on partitions of an NVIDIA GPU. This technique can be applied to any component that internally utilizes global CPU scheduling. The proposed locking protocol enables increased GPU parallelism and reduces GPU capacity loss with analytically provable benefits. 

Semi-partitioned scheduling is an approach to multiprocessor real-time scheduling where most tasks are fixed to processors, while a small subset of tasks is allowed to migrate. This approach offers reduced overhead compared to global scheduling, and can reduce processor capacity loss compared to partitioned scheduling. Prior work has resulted in a number of semi-partitioned scheduling algorithms, but their correctness typically hinges on a complex intertwining of offline task assignment and online execution. This brittleness has resulted in few proposed semi-partitioned scheduling algorithms that support dynamic task systems, where tasks may join or leave the system at runtime, and few that are optimal in any sense. This paper introduces EDF-sc, the first semi-partitioned scheduling algorithm that is optimal for scheduling (static) soft real-time (SRT) sporadic task systems and allows tasks to dynamically join and leave. The SRT notion of optimality provided by EDF-sc requires deadline tardiness to be bounded for any task system that does not cause over-utilization. In the event that all tasks can be assigned as fixed, EDF-sc behaves exactly as partitioned EDF. Heuristics are provided that give EDF-sc the novel ability to stabilize the workload to approach the partitioned case as tasks join and leave the system.