SEAMERS: A Semi-partitioned Energy-Aware scheduler for heterogeneous MulticorE Real-time Systems

Moulik, Sanjay; Das, Zinea; Devaraj, Rajesh; Chakraborty, Shounak

doi:10.1016/j.sysarc.2020.101953

Cited by 24 publications

(8 citation statements)

References 18 publications

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“…The authors in [31] have explored the evolving landscape of processing platforms in battery-supported realtime systems, which increasingly incorporate specialized multi-cores to address the demands of modern applications. It underscores the pressing need for energyefficient schedulers in such environments.…”

Section: Related Workmentioning

confidence: 99%

“…It underscores the pressing need for energyefficient schedulers in such environments. SEAMERS is a low-overhead heuristic strategy designed for dynamic voltage and frequency scaling (DVFS)-based energyaware scheduling of real-time periodic tasks on heterogeneous multi-core platforms [31]. SEAMERS operates through four phases: (i) deadline partitioning; (ii) core clustering; (iii) task allocation; and (iv) energy-aware scheduling, which collectively aim to optimize task scheduling while considering both energy consumption and real-time constraints such as deadline and response time.…”

Section: Related Workmentioning

confidence: 99%

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Energy Efficient Real-Time Tasks Scheduling on High-Performance Edge-Computing Systems Using Genetic Algorithm

Hussain,

Zakarya,

Ali

et al. 2024

IEEE Access

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With an increase in the number of processing cores or systems, the high-performance edge-computing system's power consumption along with its computational speed will increase, essentially. However, this comes at the expense of high-energy utilization. One notable solution to reduce the energy consumption of these systems is to execute these systems at the slowest feasible speed so that the job's deadline times are met. Unfortunately, this method is at the expense of more response time and performance loss. To resolve this issue, in this paper, we propose a scheduling approach that associates the genetic algorithm (GA) with the first feasible speed (FiFeS) technique i.e. GA-FiFeS algorithm. This does not jeopardize real-time tasks' deadlines. The GA-FiFeS algorithm proposes an energy-efficient schedule while still ensuring high response times. The results of the proposed approach, using plausible assumptions and experimental parameters, are compared with currently in-practice approaches, i.e. FiFeS and LeFeS (least feasible speed) approaches. Using numerical simulations and plausible assumptions, our investigation suggests that the proposed GA-FiFeS technique outperforms the FiFeS technique in terms of energy consumption (∼18.56%) and response times (∼2.78%). Furthermore, the GA-FiFeS has comparable outcomes with the LeFeS method while taking the expected time of execution as an assessment feature for analysis.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Energy Efficient Real-Time Tasks Scheduling on High-Performance Edge-Computing Systems Using Genetic Algorithm

Hussain,

Zakarya,

Ali

et al. 2024

IEEE Access

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“…This approach focuses on the allocation of tasks in a heterogeneous multicore environment while minimizing unnecessary computational burdens. SEAMERS implements dynamic voltage and frequency scaling (DVFS) on a per-core basis, enabling optimized task scheduling at the core level [22]. Sharma et.al presented a cluster based heuristic scheduling strategy named CETAS which stands for A Cluster based Energy and Temperature Efficient Real-time Scheduler for heterogeneous platforms, which performs energy as well as temperature aware task scheduling on heterogeneous multicore platforms.…”

Section: Related Workmentioning

confidence: 99%

Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform

Ali,

Khattak,

Iqbal

et al. 2023

IEEE Access

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The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clusters, the tasks are partitioned into clusters, which are scheduled on the cores within each cluster using a global approach. In this study, a cluster-based technique is adopted for scheduling tasks of real-time mixed-criticality systems (MCS). The Decreasing Criticality Decreasing Utilization with the worst-fit (DCDU-WF) technique is used for partitioning of tasks to clusters, whereas a novel mixed-criticality cluster-based boundary fair (MC-Bfair) scheduling approach is used for scheduling tasks on cores within clusters. The MC-Bfair scheduling algorithm reduces the number context switches and migration of tasks, which minimizes the overhead of mixed-criticality tasks. The migration and context switch overhead time is added at the time of each migration and context switch respectively for a task. In low critical mode, the low mode context switch and migration overhead time is added to task execution time, while the high mode overhead time of migration and context switch is added to the execution time of a task in high critical mode. The results obtained from experiments show the better schedulablity performance of proposed cluster-based technique as compared to cluster-based fixed priority (CB-FP), MC-EKG-VD-1, global and partitioned scheduling techniques e.g., for target utilization U=0.6, the proposed technique schedule 66.7% task sets while MC-EKG-VD-1, CB-FP, partitioned and global techniques schedule 50%, 33.3%, 16.7% and 0% task sets respectively.

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“…Real-time systems based on heterogeneous multi-core processors have been successfully applied in the fields of digital signal processing [2] , image processing [3][4] , and distributed systems [5] , and have achieved remarkable results. Resource allocation [6] and energy consumption [7] in the process of task scheduling based on heterogeneous multicore processor platforms are always the key issues to be faced. To address these problems, this study adopts a migration method that can avoid cache misses and processor task state transitions.…”

Section: Introductionmentioning

confidence: 99%

Worst-Case Response Time Analysis of Multitype DAG Tasks Based on Reconstruction

et al. 2022

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With the wide application of heterogeneous multi-core processor real-time systems, the existing analysis methods of worst-case response time (WCRT) overestimate the blocking information among tasks, resulting in a rather pessimistic estimation. To improve the accuracy of the WCRT, we propose a reconstruction-based WCRT analysis method for multi-type directed acyclic graph (DAG) tasks scheduling algorithm(RMDS). The RMDS algorithm comprises the following steps: First, we unitize all task nodes in the multi-type DAG task; Then, we use key factors as task priorities to schedule tasks and reconstruct the DAG task model into a parallel node segment model; Finally, we estimate the WCRT of multi-type DAG tasks according to the parallel node segment model to assess task schedulability. To verify the performance of our algorithm, we compared it with traditional algorithms. RDMS showed an acceptance rate 6.13% higher and its overall performance increased by 25.95% in comparison with traditional algorithms.

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SEAMERS: A Semi-partitioned Energy-Aware scheduler for heterogeneous MulticorE Real-time Systems

Cited by 24 publications

References 18 publications

Energy Efficient Real-Time Tasks Scheduling on High-Performance Edge-Computing Systems Using Genetic Algorithm

Energy Efficient Real-Time Tasks Scheduling on High-Performance Edge-Computing Systems Using Genetic Algorithm

Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform

Worst-Case Response Time Analysis of Multitype DAG Tasks Based on Reconstruction

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