Abstract
This paper extends the work presented in Maia et al. (Semi-partitioned scheduling of fork-join tasks using work-stealing, 2015) where we address the semi-partitioned scheduling of real-time fork-join tasks on multicore platforms. The proposed approach consists of two phases: an offline phase where we adopt a multi-frame task model to perform the task-to-core mapping so as to improve the schedulability and the performance of the system and an online phase where we use the work-stealing algorithm to exploit tasks’ parallelism among cores with the aim of improving the system responsiveness. The objective of this work is twofold: (1) to provide an alternative scheduling technique that takes advantage of the semi-partitioned properties to accommodate fork-join tasks that cannot be scheduled in any pure partitioned environment and (2) to reduce the migration overheads which has been shown to be a traditional major source of non-determinism for global scheduling approaches. In this paper, we consider different allocation heuristics and we evaluate the behavior of two of them when they are integrated within our approach. The simulation results show an improvement up to 15% of the proposed heuristic over the state-of-the-art in terms of the average response time per task set.
Highlights
Multicore platforms are very common in the embedded systems domain as they provide more computing power for the execution of complex applications with stringent timing constraints
(4) We extend the work presented in [8] by comparing different allocation heuristics in terms of their allocation behavior. For two of these heuristics, we evaluate the improvement given by using work-stealing in terms of task average response times
2 Related work Three task models supporting intra-task parallelism exist in the real-time systems domain: (1) the fork-join task model, (2) the synchronous task model, and (3) the directed acyclic graph (DAG) task model
Summary
Multicore platforms are very common in the embedded systems domain as they provide more computing power for the execution of complex applications with stringent timing constraints. (3) As the parallel regions of each fork-join task can execute simultaneously on different cores, we take advantage of the work-stealing mechanism to reduce the average response time of the tasks without jeopardizing the schedulability of the whole system. 2 Related work Three task models supporting intra-task parallelism exist in the real-time systems domain: (1) the fork-join task model, (2) the synchronous task model, and (3) the directed acyclic graph (DAG) task model. Bado et al [16] proposed a semi-partitioned approach with job-level migration for fork-join tasks, which is similar to the one in [9], but due to the assignment methods proposed in their paper for the offsets and local deadlines, they did not provide any guarantee on the fact that sub-tasks execute in parallel. While their work is similar to ours w.r.t. the adopted class of schedulers (semi-partitioned), we differ in that we relax the constraint of restricting the task parallelism and we use task-level migration instead of job-level migration, further reducing the number of migrations at runtime
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