RQSG-I: An optimized real time scheduling algorithm for tasks allocation in grid environments

Aghazarian, Vahe; Delavar, Arash Ghorbannia; Motlagh, Nima Ghazanfari; naeini, Mohsen Khajeh

doi:10.1109/iccsn.2011.6013810

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Cited by 4 publications

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“…The proposed Ant Colony Optimization (ACO) algorithms provide inherent parallelism, which is very useful in multiprocessor environments [25]. An optimized real time scheduling algorithm for tasks allocation in grid environment has proposed by Aghazarian V et al [26]. Scheduling of shared memory with multi-core performance in dynamic allocator using arm processor has implemented by Venkata Siva Prasad Ch.…”

Section: Literature Surveymentioning

confidence: 99%

Reinforcement learning based multi core scheduling (RLBMCS) for real time systems

Rinku¹,

AshaRani²

2020

IJECE

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Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system.

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Section: Literature Surveymentioning

confidence: 99%