Abstract

Abstract
Several schedulability analyses have been proposed for a variety of parallel task systems with real-time constraints. However, these analyses are mostly restricted to global scheduling policies. The problem with global scheduling is that it adds uncertainty to the lower-level timing analysis which on multicore systems are heavily context-dependent. As parallel tasks typically exhibit intense communication and concurrency among their sequential computational units, this problem is further exacerbated. This paper considers instead the schedulability of partitioned parallel tasks. More precisely, we present a response time analysis for sporadic DAG tasks atop multiprocessors under partitioned fixed-priority scheduling. We assume the partitioning to be given. We show that a partitioned DAG task can be modeled as a set of self-suspending tasks. We then propose an algorithm to traverse a DAG and characterize such worst-case scheduling scenario. With minor modifications, any state-of-the-art technique for sporadic self-suspending tasks can thus be used to derived the worstcase response time of a partitioned DAG task. Experiments show that the proposed approach significantly tightens the worst-case response time of partitioned parallel tasks comparatively to the state-of-the-art when the most accurate technique is chosen.

Abstract

Abstract
Scheduling real-time applications on general purpose multicore platforms is a challenging problem from a timing analysis perspective. Such platforms expose uncontrolled sources of interference whenever concurrent accesses to memory are performed. The non-deterministic bus and memory access behavior complicates the estimations of applications' worst-case execution times (WCET). The 3-phase task model seems a good candidate to circumvent the uncontrolled sources of interference by isolating concurrent memory accesses. A task is divided in three successive phases; first, the task loads its instruction and data in a local memory, then it executes non-preemptively using those pre-loaded instructions and data, and finally, the modified data are pushed back to main memory. Following this execution model, tasks never access the bus during their execution phase. Instead, all the bus accesses are performed during the first and third phases. In this paper, we focus on the global fixed-priority scheduling of the 3-phase task model. A new schedulability test is derived by modelling the interference happening on the bus rather than the interference on the cores as in the state-ot-the-art techniques. The effectiveness of the test is evaluated by comparing it against the state-of-the-art.

Abstract
Future real-time embedded systems will increasingly incorporate mixed application models with timing constraints running on the same multi-core platform. These application models are dataflow applications with timing constraints and traditional real-time applications modelled as independent arbitrary-deadline tasks. These systems require guarantees that all running applications execute satisfying their timing constraints. Also, to be cost-efficient in terms of design, they require efficient mapping strategies that maximize the use of system resources to reduce the overall cost. This work proposes an approach to integrate mixed application models (dataflow and traditional real-time applications) with timing requirements on the same multi-core platform. It comprises three main algorithms: 1) Slack-Based Merging, 2) Timing Parameter Extraction, and 3) Communication-Aware Mapping. Together, these three algorithms play a part in allowing mapping and scheduling of mixed application models in embedded real-time systems. The complete approach and the three algorithms presented have been validated through proofs and experimental evaluation. © 2017 Copyright held by the owner/author(s).

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.