Scheduling Parallel Real-Time Tasks on the Minimum Number of Processors

Cho, Hyeonjoong; Kim, Chul-Goo; Sun, Joohyung; Easwaran, Arvind; Park, Ju-Derk; Choi, Byeong-Cheol

doi:10.1109/tpds.2019.2929048

Cited by 16 publications

(4 citation statements)

References 39 publications

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“…In [22], the authors try to reduce the hardware cost by minimize the number of required processors to schedule an application, where considerably memory requirements and application latency are reduced by comparing with related approaches while meeting the same throughput constraint. In [23], the authors shows how to minimize the number of required processors for feasible running the parallel real-time tasks. However, our work considers to minimize security-enhancing related hardware cost of ACPS by minimizing the number of HSMs, where both security and real-time requirements are considered.…”

Section: Related Workmentioning

confidence: 99%

Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems

Xie

Guo

Yang

et al. 2021

Sensors

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The introduction of various networks into automotive cyber-physical systems (ACPS) brings great challenges on security protection of ACPS functions, the auto industry recommends to adopt the hardware security module (HSM)-based multicore ECU to secure in-vehicle networks while meeting the delay constraint. However, this approach incurs significant hardware cost. Consequently, this paper aims to reduce security enhancing-related hardware cost by proposing two efficient design space exploration (DSE) algorithms, namely, stepwise decreasing-based heuristic algorithm (SDH) and interference balancing-based heuristic algorithm (IBH), which explore the task assignment, task scheduling, and message scheduling to minimize the number of required HSMs. Experiments on both synthetical and real data sets show that the proposed SDH and IBH are superior than state-of-the-art algorithm, and the advantage of SDH and IBH becomes more obvious as the increase about the percentage of security-critical tasks. For synthetic data sets, the hardware cost can be reduced by 61.4% and 45.6% averagely for IBH and SDH, respectively; for real data sets, the hardware cost can be reduced by 64.3% and 54.4% on average for IBH and SDH, respectively. Furthermore, IBH is better than SDH in most cases, and the runtime of IBH is two or three orders of magnitude smaller than SDH and state-of-the-art algorithm.

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Section: Related Workmentioning

confidence: 99%

Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems

Xie

Guo

Yang

et al. 2021

Sensors

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“…For FFT application, the number of tasks is |N| = (2 × ρ − 1) + ρ × log 2 ρ, and ρ = 2 x where x is an integer. We can see that there are four exit tasks (task 12,13,14,15) in the FFT graph in Figure 2a, and there will be ρ exit tasks in the FFT graph with parameter ρ. In order to match the application model in Section 3.1, we add a dummy exit task whose execution time is 0.…”

Section: Fast Fourier Transform Applicationmentioning

confidence: 99%

“…Therefore, energy consumption is one of the main design constraints for such heterogeneous multi-core systems. A well-known typical mechanism for reducing power consumption of computing systems is dynamic voltage and frequency scaling (DVFS), which is realized to achieve the balance between energy consumption and performance by reducing the power supply voltage and frequency of the processor at the same time, when the task is running [1,[14][15][16][17][18][19]. Therefore, energy consumption constrained task scheduling on processors with adjustable voltage and frequency has attracted extensive research [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Task-Level Aware Scheduling of Energy-Constrained Applications on Heterogeneous Multi-Core System

et al. 2020

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Minimizing the schedule length of parallel applications, which run on a heterogeneous multi-core system and are subject to energy consumption constraints, has recently attracted much attention. The key point of this problem is the strategy to pre-allocate the energy consumption of unscheduled tasks. Previous articles used the minimum value, average value or a power consumption weight value as the pre-allocation energy consumption of tasks. However, they all ignored the different levels of tasks. The tasks in different task levels have different impact on the overall schedule length when they are allocated the same energy consumption. Considering the task levels, we designed a novel task energy consumption pre-allocation strategy that is conducive to minimizing the scheduling time and developed a novel task schedule algorithm based on it. After getting the preliminary scheduling results, we also proposed a task execution frequency re-adjustment mechanism that can re-adjust the execution frequency of tasks, to further reduce the overall schedule length. We carried out a considerable number of experiments with practical parallel application models. The results of the experiments show that our method can reach better performance compared with the existing algorithms.

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“…A parallel real-time task can be modelled as a directed acyclic graph (DAG) containing nodes and edges, where nodes and edges correspond to subtasks and their precedence constraints, respectively [1,2]. DAG tasks have many applications including cloud data centres and unmanned aerial vehicles [3]. For DAG-based real-time task models, we propose a decomposition algorithm to improve the schedulability of Earliest Deadline First (EDF), which is one of the well-known real-time scheduling algorithms.…”

mentioning

confidence: 99%

Optimising earliest deadline first scheduling for parallel real‐time tasks on multiprocessors

2019

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Multiprocessors have become prevalent in real-time systems owing to their higher throughput. Various types of scheduling algorithms have been proposed for parallel real-time tasks, which differ from traditional tasks in that their subtasks execute in parallel. A parallel task is frequently modelled as a directed acyclic graph (DAG) that expresses the precedence constraints between its subtasks. In this Letter, the authors propose a decomposition algorithm to improve the Earliest Deadline First schedulability for DAG tasks, based on convex optimisation theory. Their experimental results demonstrate that their algorithm outperforms the two most recently published algorithms.

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Scheduling Parallel Real-Time Tasks on the Minimum Number of Processors

Cited by 16 publications

References 39 publications

Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems

Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems

Task-Level Aware Scheduling of Energy-Constrained Applications on Heterogeneous Multi-Core System

Optimising earliest deadline first scheduling for parallel real‐time tasks on multiprocessors

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