ImGA: an improved genetic algorithm for partitioned scheduling on heterogeneous multi-core systems

Ayari, Rabeh; Hafnaoui, Imane; Beltrame, Giovanni; Nicolescu, Gabriela

doi:10.1007/s10617-018-9208-1

Cited by 10 publications

(6 citation statements)

References 27 publications

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“…In this section, we evaluate the performance of EDFadaptive by generating task sets for the SRT system and 𝜏 3 (3,4) 𝜏 4 (2,3) 𝜏 5 (3,5) 𝜏 6 (1,2) Journal of Sensors compare it with EDF-fm and EDF-os against five criteria as follows:…”

Section: Discussionmentioning

confidence: 99%

“…For example, τ 2 is assigned to P 1 and P 2 , and its utilization has been divided into two parts: u 2,1 = 1/10 and u 2,2 = u 2 − u 2,1 = 29/60; that means, τ 2 is a migrating task. 𝜏 3 (3,4) 𝜏 4 (2,3) 𝜏 5 (3,5) 𝜏 6 (1,2)…”

Section: Taskmentioning

confidence: 99%

“…Because the former assigns tasks statically to processors and does not allow the tasks to migrate, it cannot achieve higher system utilization whereas the latter schedules tasks from a single run queue and the task hence can be migrated [1]. Although the global approach can attain higher system utilization but migrating tasks entails higher run-time overheads, especially in soft real-time (SRT) systems, many approaches have been proposed, based on these two algorithms [2][3][4][5][6] or the hybrid of the two [7][8][9][10][11][12][13][14][15][16][17][18]. Some approaches are aimed at reducing the switch context or preemption overheads and achieving higher system utilization [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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EDF‐Adaptive: A New Semipartitioned Scheduling Algorithm for Multiprocessor Real‐Time

Huang

et al. 2021

Journal of Sensors

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Most of the multiprocessor real-time scheduling algorithms follow the partitioned approach, the global approach, or the semipartitioned approach which is a hybrid of the first two by allowing a small subset of tasks to migrate. EDF-fm (Earliest Deadline First-based Fixed and Migrating) and EDF-os (Earliest Deadline First-based Optimal Semipartitioned) are semipartitioned approaches and were proposed for soft real-time sporadic task systems. Despite their desirable property that migrations are boundary-limited such as they can only occur at job boundaries, EDF-fm and EDF-os are not always optimal and have higher tardiness and cost of overheads due to task migration. To address these issues, in this paper, we classify the systems into different types according to the utilization of their tasks and propose a new semipartitioned scheduling algorithm, earliest deadline first-adaptive, dubbed as EDF-adaptive. Our experiments show that EDF-adaptive can achieve better performance than EDF-fm and EDF-os, in terms of system utilization and tardiness overhead. It is also proved that EDF-adaptive is able to lessen the task migration overhead, by reducing the number of migrating jobs and the number of processors to which a task is migrated.

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Section: Discussionmentioning

confidence: 99%

Section: Taskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EDF‐Adaptive: A New Semipartitioned Scheduling Algorithm for Multiprocessor Real‐Time

Huang

et al. 2021

Journal of Sensors

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“…Static scheduling algorithms are usually based on metaheuristic algorithms such as generic algorithm (GA), ant colony optimization (ACO), and bee colony algorithm. (e.g., [30], [31] [32], [34]) Job shop scheduling (e.g., [18]- [21]) is an active area of task scheduling research, where the schedule for a set of production jobs is planned before the production and this scheduling can be done with static scheduling schemes. Dynamic scheduling is performed when the applications are mapped in an on-line fashion, (e.g., when tasks' arrival are unpredictable) and are mapped as they arrive (the workload is not known a priori).…”

Section: Introductionmentioning

confidence: 99%

A Method to Construct Task Scheduling Algorithms for Heterogeneous Multi-Core Systems

Kim

2019

IEEE Access

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The use of heterogeneous multicore processors (HMP) is spreading rapidly from data centers to large-scale deployment in smartphones because they give greater flexibility to adapt to power constraints and performance needs. In this study, we show that an intelligent task scheduler is critical for improving the performance and energy efficiency in an HMP environment. We assume that the tasks are independent in the environment, with hard real-time constraints and multicore systems, where the processors can be manipulated to change the clock cycle speed and power levels. Tasks are assumed to arrive aperiodically where the tasks are applications from the SPEC CPU 2006 benchmark suite. In the evaluation, we used a real system comprising of two multicore processors, which supported on-the-fly dynamic voltage/frequency scaling. We extracted several important components from previously proposed algorithms and combined them to construct algorithms with better performance. Our results showed that some of the best combinations reduced the energy consumption and achieved a better completion rate in the environment. In addition, a method is proposed for calculating the upper-bound of the task completion rate and energy consumption so that there is a guide as to how near the results are to the optimal performance. INDEX TERMS Task scheduling, energy-aware scheduling, heterogeneous multi-core, real-time scheduling, dynamic voltage/frequency scaling.

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“…El tiempo total de ejecución, el coste de las comunicaciones entre tareas y el consumo de memoria se expresan en funciones de adecuación que han de ser minimizadas. En el siguiente trabajo(Ayari et al, 2018), se presenta un algoritmo genético avanzado (ImGA) que implementa un operador de cruce basado en la planificabilidad de las tareas(Ayari et al, 2016b), lo cual mejora la precisión de los algoritmos genéticos empleados anteriormente.De los trabajos que emplean algoritmos genéticos recogidos puede destacarse que este enfoque es utilizado habitualmente como optimizador multiobjetivo, ya que busca soluciones en las que más de un parámetro ha de ser minimizado (costes, número de computadores...).3.2. Búsqueda Tabú (TS)En(Porto et al, 2000) se presenta el uso de TS para desplegar tareas con relaciones de precedencia en procesadores y crear planificaciones cíclicas para sistemas multiprocesadores heterogéneos y de tiempo real, compuestos por computadores idénticos salvo uno de mayor capacidad de procesamiento.…”

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Una revisión de técnicas para la optimización del despliegue y planificación de sistemas de tiempo real distribuidos

Amurrio

Azketa

Gutiérrez

et al. 2019

Rev. iberoam. autom. inform. ind.

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<p>En las últimas tres décadas, se ha realizado un gran número de propuestas sobre la optimización del despliegue y planificación de sistemas de tiempo real distribuidos bajo diferentes enfoques algorítmicos que aportan soluciones aceptables a este problema catalogado como NP-difícil. En la actualidad, la mayor parte de los sistemas utilizados en el sector industrial son sistemas de criticidad mixta en los que se puede usar la planificación cíclica, las prioridades fijas y el particionado, que proporciona aislamiento temporal y espacial a las aplicaciones. Así, en este artículo se realiza una revisión de los trabajos publicados sobre este tema y se presenta un análisis de las diferentes soluciones aportadas para sistemas de tiempo real distribuidos basados en las políticas de<br />planificación que se están usando en la práctica. Como resultado de la comparación, se presenta una tabla a modo de guía en la que se relacionan los trabajos revisados y se caracterizan sus soluciones.</p>

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ImGA: an improved genetic algorithm for partitioned scheduling on heterogeneous multi-core systems

Cited by 10 publications

References 27 publications

EDF‐Adaptive: A New Semipartitioned Scheduling Algorithm for Multiprocessor Real‐Time

EDF‐Adaptive: A New Semipartitioned Scheduling Algorithm for Multiprocessor Real‐Time

A Method to Construct Task Scheduling Algorithms for Heterogeneous Multi-Core Systems

Una revisión de técnicas para la optimización del despliegue y planificación de sistemas de tiempo real distribuidos

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