Tasks Scheduling Through Hybrid Genetic Algorithm in Real-Time System on Heterogeneous Environment

Chauhan, Nutan Kumari; Tyagi, Isha; Kumar, Harendra; Sharma, Dipa

doi:10.1007/s42979-021-00959-0

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…In the research on the fault-tolerant scheduling algorithm of RT periodic tasks in heterogeneous distributed systems based on discrete logarithmic multi-signature, many scholars have studied it and achieved good results. The model is based on a rigorous method that can accurately analyze and predict the characteristics of RT task scheduling [3]. Ghanavati S proved that the RMS algorithm is the best algorithm in a RT task system whose execution time is equal to the deadline.…”

Section: Distributed Processing Systemmentioning

confidence: 99%

Fault-tolerant Scheduling Algorithm for Real-time Periodic Tasks in Heterogeneous Distributed Systems Based on Discrete Logarithmic Multi-signature

2022

DPS

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Heterogeneous processors have high performance and low energy consumption, so they are widely used in various real-time(RT) systems, such as flight control systems. In a RT system, tasks that fail to meet deadlines can have catastrophic consequences. In order to avoid the situation that the system failure causes the task to miss the deadline, it is necessary to provide the system with fault tolerance. Fault-tolerant scheduling is an effective way to achieve system fault tolerance. There are two types of system faults: transient faults and permanent faults. The purpose of this paper is to study the fault-tolerant scheduling algorithm of RT periodic tasks in heterogeneous distributed systems based on discrete logarithmic multi-signature. This paper studies the transient fault tolerance problem. Existing transient fault-tolerant algorithms only consider fault-tolerance and ignore the RT nature of tasks. Therefore, considering both fault tolerance and time constraints, this paper proposes a transient fault-tolerant scheduling algorithm DB-FTSA based on time deadlines. According to the time deadline, calculate the number of fault-tolerant tasks that can provide fault tolerance, and give priority to fault tolerance of high-priority tasks. Configuration improves system reliability. At the same time, the algorithm fully considers the setup time and task execution time, making the algorithm closer to reality and more accurate. In order to overcome the problem that the traditional dynamic programming algorithm does not support a single type of sub-module, the algorithm uses active and passive sub-modules to achieve fault tolerance to reduce sub-module redundancy. It has been experimentally proven that, therefore, large tasks should be replaced with as many small tasks as possible to reduce the number of processors required.

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Section: Distributed Processing Systemmentioning

confidence: 99%

Fault-tolerant Scheduling Algorithm for Real-time Periodic Tasks in Heterogeneous Distributed Systems Based on Discrete Logarithmic Multi-signature

2022

DPS

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“…Table 1 summarized the different techniques used in modeling the HFS. [22] 2021 Vehicular Ad-Hoc network Optimization of system complexity and accuracy 13 [15] 2022 Self-organized Optimization of system complexity 14 [10] 2023 Deep neural fuzzy system Optimization of system complexity and accuracy 15 [23] 2023 Fuzzy c-means clustering Optimization of system accuracy At present, many studies have proved that it is effective to use the global search ability of heuristic algorithms such as genetic algorithm, particle swarm optimization algorithm and differential evolution to learn the antecedent and consequent parameters of rules when building a rule base of complex fuzzy system [24][25][26][27][28]. Velliangiri et al [4] used the Taylor series and elephant herding optimization algorithm to optimize the fuzzy classifier.…”

Section: Introductionmentioning

confidence: 99%

Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

Zhu,

Zhao

2024

CMES

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The optimization of the rule base of a fuzzy logic system (FLS) based on evolutionary algorithm has achieved notable results. However, due to the diversity of the deep structure in the hierarchical fuzzy system (HFS) and the correlation of each sub fuzzy system, the uncertainty of the HFS's deep structure increases. For the HFS, a large number of studies mainly use fixed structures, which cannot be selected automatically. To solve this problem, this paper proposes a novel approach for constructing the incremental HFS. During system design, the deep structure and the rule base of the HFS are encoded separately. Subsequently, the deep structure is adaptively mutated based on the fitness value, so as to realize the diversity of deep structures while ensuring reasonable competition among the structures. Finally, the differential evolution (DE) is used to optimize the deep structure of HFS and the parameters of antecedent and consequent simultaneously. The simulation results confirm the effectiveness of the model. Specifically, the root mean square errors in the Laser dataset and Friedman dataset are 0.0395 and 0.0725, respectively with rule counts of rules is 8 and 12, respectively. When compared to alternative methods, the results indicate that the proposed method offers improvements in accuracy and rule counts.

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Hybrid optimized task scheduling with multi-objective framework for crowd sensing in mobile social networks

Ramachandran

2024

Peer-to-Peer Netw. Appl.

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Tasks Scheduling Through Hybrid Genetic Algorithm in Real-Time System on Heterogeneous Environment

Cited by 4 publications

References 43 publications

Fault-tolerant Scheduling Algorithm for Real-time Periodic Tasks in Heterogeneous Distributed Systems Based on Discrete Logarithmic Multi-signature

Fault-tolerant Scheduling Algorithm for Real-time Periodic Tasks in Heterogeneous Distributed Systems Based on Discrete Logarithmic Multi-signature

Deep Structure Optimization for Incremental Hierarchical Fuzzy Systems Using Improved Differential Evolution Algorithm

Hybrid optimized task scheduling with multi-objective framework for crowd sensing in mobile social networks

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