<p>Optimizing the performance of cloud computing space systems is an intricate problem that requires novel approaches. To effectively resolve this problem, this work examines the convergence of the genetic algorithm and stochastic Petri nets simulation modeling (SPNSM). The cloud computing space system’s SPNSM examines the system’s dynamic behavior and determines which subsystem is more crucial and requires greater maintenance. The primary goal of this paper is to analyze the stochastic dynamic behavior of the cloud computing space system and evaluate the performance matrices of each subsystem inside the system. Furthermore, the system’s performance is optimized, and the optimal value of the failure and repair rate parameters is analyzed. The stochastic dynamic behavior of the system is examined by employing stochastic Petri net modeling. The investigation was conducted using the licensed version of the Petri module GRIF<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13198_2024_2695_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$-$</EquationSource> <EquationSource Format="MATHML"><math> <mo>-</mo> </math></EquationSource> </InlineEquation>2023.7 software. Optimizing the system’s performance is achieved by employing a metaheuristic genetic algorithm. In this particular model, the genetic algorithm is implemented using the Matlab-R2022b program to evaluate its performance. An analysis of the observation results indicates that the networking subsystem is more crucial and requires greater maintenance. Analysis of the results indicates that the system performance is optimized by 95.22% at a generation size of 470 utilizing the genetic algorithm. The analysis indicates that the presence of two repair persons is adequate for the reconstruction of the cloud space system in the event of any failure in the subsystems. From the analysis of the result, it is concluded that the networking subsystem of the cloud space system requires more maintenance or repair. The basis of the information of the maintenance strategy the performance of the cloud space system can improve. It is advised that two repair specialists or repair machines be adequate for the task; this information could reduce the cost of additional labor. By analyzing the research findings, the system manager can achieve optimal plant availability.</p>

Stochastic modeling and performance optimization of the cloud computing space system utilizing Petri nets simulation modeling and genetic algorithm

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发表日期：

2025年1月8日

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Urvashi,
Shikha Bansal

摘要

Optimizing the performance of cloud computing space systems is an intricate problem that requires novel approaches. To effectively resolve this problem, this work examines the convergence of the genetic algorithm and stochastic Petri nets simulation modeling (SPNSM). The cloud computing space system’s SPNSM examines the system’s dynamic behavior and determines which subsystem is more crucial and requires greater maintenance. The primary goal of this paper is to analyze the stochastic dynamic behavior of the cloud computing space system and evaluate the performance matrices of each subsystem inside the system. Furthermore, the system’s performance is optimized, and the optimal value of the failure and repair rate parameters is analyzed. The stochastic dynamic behavior of the system is examined by employing stochastic Petri net modeling. The investigation was conducted using the licensed version of the Petri module GRIF $-$ $-$ 2023.7 software. Optimizing the system’s performance is achieved by employing a metaheuristic genetic algorithm. In this particular model, the genetic algorithm is implemented using the Matlab-R2022b program to evaluate its performance. An analysis of the observation results indicates that the networking subsystem is more crucial and requires greater maintenance. Analysis of the results indicates that the system performance is optimized by 95.22% at a generation size of 470 utilizing the genetic algorithm. The analysis indicates that the presence of two repair persons is adequate for the reconstruction of the cloud space system in the event of any failure in the subsystems. From the analysis of the result, it is concluded that the networking subsystem of the cloud space system requires more maintenance or repair. The basis of the information of the maintenance strategy the performance of the cloud space system can improve. It is advised that two repair specialists or repair machines be adequate for the task; this information could reduce the cost of additional labor. By analyzing the research findings, the system manager can achieve optimal plant availability.