A deadlock resolution strategy based on spiking neural P systems

Pang, Shanchen; Hong-qi, Chen; Líu, Hao; Yao, Jiamin; Wang, Min

doi:10.1007/s12652-019-01223-3

Cited by 13 publications

(10 citation statements)

References 26 publications

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“…The SNPS models also have been used to solve many problems, such as the identification of nuclear export signal [35], computing morphisms [65,66], intrusion detection [84], solving deadlock resolution problems [85], programmable stage controller stage programming [68] and µ-fluidic biochip design [67]. In this section, we briefly discuss the use of SNPS in solving these problems.…”

Section: Other Applicationsmentioning

confidence: 99%

“…(4) SNPS models have also been used to solve problems in operating and database systems. In [85], Pang et al proposed a method based on SNPS for solving the Deadlock resolution problem in 2019. In [85], SNPS were used for the revocation of the deadlock processes.…”

Section: Other Applicationsmentioning

confidence: 99%

“…In [85], Pang et al proposed a method based on SNPS for solving the Deadlock resolution problem in 2019. In [85], SNPS were used for the revocation of the deadlock processes. Moreover, in comparison with exhaustive method and time cost method, (i.e., traditional deadlock methods) the method proposed in [85] has significantly improved the time complexity while performing the deadlock revocation process.…”

Section: Other Applicationsmentioning

confidence: 99%

“…In [85], SNPS were used for the revocation of the deadlock processes. Moreover, in comparison with exhaustive method and time cost method, (i.e., traditional deadlock methods) the method proposed in [85] has significantly improved the time complexity while performing the deadlock revocation process. Additionally, this method improves the deadlock resolution efficiency.…”

Section: Other Applicationsmentioning

confidence: 99%

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On Applications of Spiking Neural P Systems

Fan

Paul

et al. 2020

Applied Sciences

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Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations.

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Section: Other Applicationsmentioning

confidence: 99%

Section: Other Applicationsmentioning

confidence: 99%

Section: Other Applicationsmentioning

confidence: 99%

Section: Other Applicationsmentioning

confidence: 99%

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On Applications of Spiking Neural P Systems

Fan

Paul

et al. 2020

Applied Sciences

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“…After the end of the iteration, the current round of statistical calculation information and model accuracy are output. The deadlock can be effectively avoided by using this iterative method [34], [35]. The performance statistics are used to determine whether to perform an early stop, then, the next round of iteration begins.…”

Section: B Task Scheduling Systemmentioning

confidence: 99%

Scheduling-Efficient Framework for Neural Network on Heterogeneous Distributed Systems and Mobile Edge Computing Systems

et al. 2019

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As the volume of machine learning training data sets and the quantity of model parameters continue to grow, the pattern in which machine learning models are trained alone can no longer accommodate large-scale data environments. However, distributed systems and mobile edge computing systems are unpredictable and have heterogeneous nodes, resulting in interruptions in training or low convergence rate. In addition, existing distributed machine learning frameworks cannot guarantee a good convergence rate and speedup ratio in a variety of operating environments. Considering the above shortcomings, this paper proposes an adaptive scheduling framework for machine learning based on a heterogeneous distributed system and mobile edge computing system for machine learning model optimization. The framework detects and analyzes the dynamic changes of resources in the distributed system and mobile edge computing system through the resource detection system; then, the task scheduling system adaptively modifies the environmental parameters and schedules calculations. Relevant experiments conducted with the public data set show that the robustness and scalability of the framework are significantly better than the traditional distributed machine learning framework under the premise of ensuring high convergence rate. INDEX TERMS Heterogeneous distributed system, mobile edge computing system, adaptive scheduling, large-scale machine learning.

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