Optimal Selection of Preemption Points to Minimize Preemption Overhead

A central issue for verifying the schedulability of hard real-time systems is the correct evaluation of task execution times. These values are significantly influenced by the preemption overhead, which mainly includes the cache related delays and the context switch times introduced by each preemption. Since such an overhead significantly depends on the particular point in the code where preemption takes place, this paper proposes a method for placing suitable preemption points in each task in order to maximize the chances of finding a schedulable solution. In a previous work, we presented a method for the optimal selection of preemption points under the restrictive assumption of a fixed preemption cost, identical for each preemption point. In this paper, we remove such an assumption, exploring a more realistic and complex scenario where the preemption cost varies throughout the task code. Instead of modeling the problem with an integer programming formulation, with exponential worst-case complexity, we derive an optimal algorithm that has a linear time and space complexity. This somewhat surprising result allows selecting the best preemption points even in complex scenarios with a large number of potential preemption locations. Experimental results are also presented to show the effectiveness of the proposed approach in increasing the system schedulability

Limited Preemptive Scheduling for Real-Time Systems: a Survey

The question whether preemptive algorithms are better than nonpreemptive ones for scheduling a set of real-time tasks has been debated for a long time in the research community. In fact, especially under fixed priority systems, each approach has advantages and disadvantages, and no one dominates the other when both predictability and efficiency have to be taken into account in the system design. Recently, limited preemption models have been proposed as a viable alternative between the two extreme cases of fully preemptive and nonpreemptive scheduling. This paper presents a survey of the existing approaches for reducing preemptions and compares them under different metrics, providing both qualitative and quantitative performance evaluations

An Analysis of Lazy and Eager Limited Preemption Approaches under DAG-Based Global Fixed Priority Scheduling

DAG-based scheduling models have been shown to effectively express the parallel execution of current many-core heterogeneous architectures. However, their applicability to real-time settings is limited by the difficulties to find tight estimations of the worst-case timing parameters of tasks that may arbitrarily be preempted/migrated at any instruction. An efficient approach to increase the system predictability is to limit task preemptions to a set of pre-defined points. This limited preemption model supports two different preemption approaches, eager and lazy, which have been analyzed only for sequential task-sets. This paper proposes a new response time analysis that computes an upper bound on the lower priority blocking that each task may incur with eager and lazy preemptions. We evaluate our analysis with both, synthetic DAG-based task-sets and a real case-study from the automotive domain. Results from the analysis demonstrate that, despite the eager approach generates a higher number of priority inversions, the blocking impact is generally smaller than in the lazy approach, leading to a better schedulability performance.This work was funded by the EU projects P-SOCRATES (FP7-ICT-2013-10) and HERCULES (H2020/ICT/2015/688860), and the Spanish Ministry of Science and Innovation under contract TIN2015-65316-P.Peer ReviewedPostprint (author's final draft

Timing Analysis for DAG-based and GFP Scheduled Tasks

Modern embedded systems have made the transition from single-core to multi-core architectures, providing performance improvement via parallelism rather than higher clock frequencies. DAGs are considered among the most generic task models in the real-time domain and are well suited to exploit this parallelism. In this paper we provide a schedulability test using response-time analysis exploiting exploring and bounding the self interference of a DAG task. Additionally we bound the interference a high priority task has on lower priority ones

PREM-Based Optimal Task Segmentation Under Fixed Priority Scheduling

Recently, a large number of works have discussed scheduling tasks consisting of a sequence of memory phases, where code and data are moved between main memory and local memory, and computation phases, where the task executes based on the content of local memory only; the key idea is to prevent main memory contention by scheduling the memory phase of one task in parallel with computation phases of tasks running on other cores. This paper provides two main contributions: (1) we present a compiler-level tool, based on the LLVM intermediate representation, that automatically converts a program into a conditional sequence of segments comprising memory and computation phases; (2) we propose an algorithm to find optimal segmentation decisions for a task set scheduled according to a fixed-priority partitioned scheme. Our evaluation shows that the proposed framework can be feasibly applied to realistic programs, and vastly overperforms a baseline greedy approach

Fixed-Priority Memory-Centric Scheduler for COTS-Based Multiprocessors

Memory-centric scheduling attempts to guarantee temporal predictability on commercial-off-the-shelf (COTS) multiprocessor systems to exploit their high performance for real-time applications. Several solutions proposed in the real-time literature have hardware requirements that are not easily satisfied by modern COTS platforms, like hardware support for strict memory partitioning or the presence of scratchpads. However, even without said hardware support, it is possible to design an efficient memory-centric scheduler. In this article, we design, implement, and analyze a memory-centric scheduler for deterministic memory management on COTS multiprocessor platforms without any hardware support. Our approach uses fixed-priority scheduling and proposes a global "memory preemption" scheme to boost real-time schedulability. The proposed scheduling protocol is implemented in the Jailhouse hypervisor and Erika real-time kernel. Measurements of the scheduler overhead demonstrate the applicability of the proposed approach, and schedulability experiments show a 20% gain in terms of schedulability when compared to contention-based and static fair-share approaches