Generating Utilization Vectors for the Systematic Evaluation of Schedulability Tests

Griffin, David; Bate, Iain; Davis, Robert I.

doi:10.1109/rtss49844.2020.00018

Cited by 47 publications

(21 citation statements)

References 22 publications

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“…For any frame, its H-WCET is computed by linear scaling its L-WCET (i.e., C H i,j = ξ × C L i,j ), where ξ is an input parameter. (Griffin's [38] more sophisticated H-WCET generation approach came too recently for us to consider.) The total WCET is divided between memory access time and the CPU computation time using an input parameter γ.…”

Section: Discussionmentioning

confidence: 99%

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Response time analysis of memory-bandwidth-regulated multiframe mixed-criticality systems

Hussain

Awan

Souto

et al. 2022

Journal of Systems Architecture

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Section: Discussionmentioning

confidence: 99%

“…As for other modes, the arguments of stall are easily derived from the RHS of (39), with the help of component-wise cumulative functions (37) and (38).…”

Section: Ammc-max Exhaustive Analysismentioning

confidence: 99%

Response time analysis of memory-bandwidth-regulated multiframe mixed-criticality systems

Hussain

Awan

Souto

et al. 2022

Journal of Systems Architecture

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“…For each taskset, we set the initial desired utilization equal to 0.25, 0.5, and 0.75. The utilization for each task was then was derived using the algorithm proposed by Griffin et al [16]. The initial desired periods were chosen at random from a normal distribution in the range [10,100].…”

Section: Discussionmentioning

confidence: 99%

Scheduling Elastic Applications in Compositional Real-Time Systems

Salman

Mubeen

Markovic

et al. 2021

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

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Many real-time applications have functional behaviour that requires variability in timing properties at runtime. The elastic task model provides a convenient mechanism to specify and encapsulate such variability and enables the modification of an application's periods during run-time to keep the application schedulable. Additionally, reservation-based scheduling techniques were proposed for the same purpose of taming unpredictability of timing variations, but with a different solution, i.e., by providing the spatial and temporal isolation for executing independent applications on the same hardware.In this paper, we combine the two approaches by proposing a two-level adaptive scheduling framework which is based on the elastic task model and the compositional framework based on the periodic resource model. The proposed framework minimises the number of requests for bandwidth adaption at the reservation (system) level and primarily enables schedulability by accounting for the application's elasticity by adjusting the periods. The motivation for this design choice is to rather localise the effect of the modifications within the application, without necessarily affecting all the applications at the system level compared to the changes made at the application level. The evaluation results show that the local application changes may often be enough to solve the problem of variability, significantly reducing the number of bandwidth adjustments, and therefore reducing the potential negative impact on all the applications of a system.

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“…-Task utilizations ( U i = C i ∕T i ) were generated using the Dirichlet-Rescale (DRS) algorithm (Griffin et al 2020b) (open source Python software (Griffin et al 2020a)) providing an unbiased distribution of utilization values that sum to the total utilization U required. -Task periods T i were generated according to a log-uniform distribution (Emberson et al 2010) with a factor of 100 difference between the minimum and maximum possible period.…”

Section: Task Set Parameter Generationmentioning

confidence: 99%

A framework for multi-core schedulability analysis accounting for resource stress and sensitivity

2022

Self Cite

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Timing verification of multi-core systems is complicated by contention for shared hardware resources between co-running tasks on different cores. This paper introduces the Multi-core Resource Stress and Sensitivity (MRSS) task model that characterizes how much stress each task places on resources and how much it is sensitive to such resource stress. This model facilitates a separation of concerns, thus retaining the advantages of the traditional two-step approach to timing verification (i.e. timing analysis followed by schedulability analysis). Response time analysis is derived for the MRSS task model, providing efficient context-dependent and context independent schedulability tests for both fixed priority preemptive and fixed priority non-preemptive scheduling. Dominance relations are derived between the tests, along with complexity results, and proofs of optimal priority assignment policies. The MRSS task model is underpinned by a proof-of-concept industrial case study. The problem of task allocation is considered in the context of the MRSS task model, with Simulated Annealing shown to provide an effective solution.

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Generating Utilization Vectors for the Systematic Evaluation of Schedulability Tests

Cited by 47 publications

References 22 publications

Response time analysis of memory-bandwidth-regulated multiframe mixed-criticality systems

Response time analysis of memory-bandwidth-regulated multiframe mixed-criticality systems

Scheduling Elastic Applications in Compositional Real-Time Systems

A framework for multi-core schedulability analysis accounting for resource stress and sensitivity

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