HLBS: Heterogeneous Laxity-Based Scheduling Algorithm for DAG-Based Real-Time Computing

Suzuki, Yuhei; Azumi, Takuya; Nobuhiko,; Nishio,; Kato, Shigeo

doi:10.1109/cpsna.2016.25

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…In the third step, a reasonable time constraint (i.e., laxity) is given to each job based on JLD and the end-to-end deadline. The laxity value represents the margin to the endto-end deadline for each job [9], [19] and can be calculated by recursively subtracting the communication time between jobs and the job execution time from the data flow's exit node job deadline. Therefore, the laxity value of any job is the start time threshold to meet the end-to-end deadline for the exit node job at the end of the data flow connected by joblevel dependencies.…”

Section: Allocate Time Constraint To Each Jobmentioning

confidence: 99%

“…The priority of each job in scheduling is the second application. The laxity is a metric that represents the margin to the end-to-end deadline for each job, and heuristic task scheduling algorithms that prioritize the laxity have been proposed [9], [19]. Prioritizing jobs with lower margins, i.e., jobs with lower laxity values, lower the deadline miss ratio.…”

Section: How To Use the Proposal Laxity Valuementioning

confidence: 99%

See 1 more Smart Citation

Deadline Miss Early Detection Method for Mixed Timer-Driven and Event-Driven DAG Tasks

Yano

Azumi

2023

IEEE Access

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Self-driving systems have a hard real-time nature, and the control commands of the vehicle must be output before the end-to-end deadline using sufficiently new data. It must also have the functionality to quickly shift to a safe state in the event of a deadline miss. However, the current self-driving system can only detect a deadline miss at the end of the process. To further improve safety, a method for detecting the possibility of a deadline miss in the middle of the process is required. Therefore, we represent such a real-time system as a mixed timer-driven and event-driven directed acyclic graph (DAG) and propose an early detection method for deadline misses by deriving a time constraint for each node. The experimental evaluation shows that the proposed method can detect deadline misses early for various scheduling algorithms. It also shows that the deadline miss ratio can be reduced by prioritizing the scheduling of jobs with a small margin to the time constraint determined using the proposed method for each job.INDEX TERMS DAG, deadline miss early detection, event-driven task, self-driving system, timer-driven task

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Section: Allocate Time Constraint To Each Jobmentioning

confidence: 99%

Section: How To Use the Proposal Laxity Valuementioning

confidence: 99%

Deadline Miss Early Detection Method for Mixed Timer-Driven and Event-Driven DAG Tasks

Yano

Azumi

2023

IEEE Access

Self Cite

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“…Synch nodes are then inserted before the tasks to handle buffered data. In this way, a single-rate DAG is obtained and scheduled using a fixed-priority algorithm based on the HLBS scheduler [17].…”

Section: B Multi-rate Dagmentioning

confidence: 99%

“…The ready queue is sorted in increasing LFT order (line 9). A ready job is scheduled if a processor is available and if its execution time, starting from the current t, does not exceed its LFT (lines 13,16,17). If Fig.…”

Section: B Schedulabilitymentioning

confidence: 99%

Latency-Aware Generation of Single-Rate DAGs from Multi-Rate Task Sets

Verucchi

Theile

Caccamo

et al. 2020

2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)

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Modern automotive and avionics embedded systems integrate several functionalities that are subject to complex timing requirements. A typical application in these fields is composed of sensing, computation, and actuation. The ever increasing complexity of heterogeneous sensors implies the adoption of multi-rate task models scheduled onto parallel platforms. Aspects like freshness of data or first reaction to an event are crucial for the performance of the system. The Directed Acyclic Graph (DAG) is a suitable model to express the complexity and the parallelism of these tasks. However, deriving age and reaction timing bounds is not trivial when DAG tasks have multiple rates. In this paper, a method is proposed to convert a multi-rate DAG task-set with timing constraints into a single-rate DAG that optimizes schedulability, age and reaction latency, by inserting suitable synchronization constructs. An experimental evaluation is presented for an autonomous driving benchmark, validating the proposed approach against state-of-the-art solutions.

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“…Nodes with no dependency can be executed in parallel, e.g., node v2, v3 and v4. Many existing works use DAGs to model the system [3], [4], [5], [6], [7], [8], [9]. For example, Verucchi et al [3] models a complete automotive task chain from perception to control as a DAG task.…”

Section: Introductionmentioning

confidence: 99%

DAG Scheduling and Analysis on Multi-Core Systems by Modelling Parallelism and Dependency

Zhao

Dai

Bate

2022

IEEE Trans. Parallel Distrib. Syst.

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With ever more complex functionalities being implemented in emerging real-time applications, multi-core systems are demanded for high performance, with directed acyclic graphs (DAG) being used to model functional dependencies. For a single DAG task, our previous work presented a concurrent provider and consumer (CPC) model that captures the node-level dependency and parallelism, which are the two key factors of a DAG. Based on the CPC, scheduling and analysis methods were constructed to reduce makespan and tighten the analytical bound of the task. However, the CPC-based methods cannot support multi-DAGs as the interference between DAGs (i.e., inter-task interference) is not taken into account. To address this limitation, this paper proposes a novel multi-DAG scheduling approach which specifies the number of cores a DAG can utilise so that it does not incur the inter-task interference. This is achieved by modelling and understanding the workload distribution of the DAG and the system. By avoiding the inter-task interference, the constructed schedule provides full compatibility for the CPC-based methods to be applied on each DAG and reduces the pessimism of the existing analysis. Experimental results show that the proposed multi-DAG method achieves an improvement up to 80% in schedulability against the original work that it extends, and outperforms the existing multi-DAG methods by up to 60% for tightening the interference.

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HLBS: Heterogeneous Laxity-Based Scheduling Algorithm for DAG-Based Real-Time Computing

Cited by 11 publications

References 11 publications

Deadline Miss Early Detection Method for Mixed Timer-Driven and Event-Driven DAG Tasks

Deadline Miss Early Detection Method for Mixed Timer-Driven and Event-Driven DAG Tasks

Latency-Aware Generation of Single-Rate DAGs from Multi-Rate Task Sets

DAG Scheduling and Analysis on Multi-Core Systems by Modelling Parallelism and Dependency

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