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A computing resources prediction approach based on ensemble learning for complex system simulation in cloud environment

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simulation MODELLING PRACTICE AND THEORY 2021年 107卷 102202-102202页

作者： Wang, Shuai Zhu, Feng Yao, Yiping Tang, Wenjie Xiao, Yuhao Xiong, Siqi Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

Cloud computing provides a new infrastructure for the research of complex system simulation (CSS). However, insufficient computing resource allocation results in lower performance of a CSS application. On the other hand, excessive computing resource allocation will lead to the increase of simulation communication overhead and simulation synchronous computing. Therefore, accurate computing resource prediction is important to achieve optimal scheduling for CSS applications in the cloud environment. In this paper, a computing resource prediction approach based on ensemble learning has been proposed, which includes a cloud computing resource prediction framework and an intelligent ensemble algorithm. The framework with three-level architecture (simulation as a service, cloud computing resource predictor, and cloud computing resource pool) can provide computing resources to deal with the demands of the simulation applications. The intelligent ensemble algorithm uses an Accuracy and Relative Error-based Pruning algorithm to ensure the effective ensemble of base models (support vector machine, decision tree, and k-nearest neighbor). To improve the performance of the intelligent ensemble algorithm, a Feature Capability-based forward search Feature Selection algorithm is introduced to reduce redundancy between features. The experiments are presented to demonstrate that the intelligent ensemble algorithm can achieve higher accuracy by 4%-20% when compared with existing resource prediction models such as Regressive Ensemble Approach for Prediction, Bayesian, Linear Regression, Random Forest, and Fuzzy Neural Network.

关键词： complex system simulation Cloud computing Computing resource prediction Ensemble learning

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FLAME GPU: complex system simulation Framework 15

FLAME GPU: Complex System Simulation Framework

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15th International Conference on High Performance Computing & simulation (HPCS)

作者： Richmond, Paul Chimeh, Mozhgan K. Univ Sheffield Dept Comp Sci 211 Portobello Sheffield S1 4DP S Yorkshire England

ISBN: (纸本)9781538632505

FLAME GPU is an agent based simulation framework that utilises the parallel architecture of Graphic Processing Unit (GPU) to enable real time model interaction and visualisation. In this paper, we provide an overview of the features of FLAME GPU and demonstrate its efficiency as a parallel agent based simulation platform. FLAME GPU abstracts the complexity of the GPU architecture from the users by offering a high level modelling syntax based on a formal state machine representation. A flocking model is presented showing how a simple multi-agent system is modelled.

关键词： GPU ABM complex system simulation Agent Based Modelling

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A human hybrid tracking and localization method for mixed reality simulation of complex system

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International Journal of Modeling, simulation, and Scientific Computing 2022年第2期13卷 187-199页

作者： Yufen Wu Yi Lv Dawei Wang Yang Xue Shuhong Xu Beijing Aircraft Technology Research Institute Comac Beijing 102211P.R.China

In order to obtain stable and high-precision motion tracking in mixed reality(MR)simulation of the complex system,an optical-inertial hybrid tracking method is proposed,which combines the accuracy of optical motion capture equipment and the robustness of inertial motion capture equipment,and can reduce the occlusion problem of entity scene on the premise of ensuring the localization *** method has been applied to the ergonomic evaluation of large civil aircraft development processes,and the effectiveness and usability of the method have been verified.

关键词： complex system simulation motion tracking optical-inertial fusion mixed reality scene

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A time-sensitive learning-to-rank approach for cloud simulation resource prediction

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complex & INTELLIGENT systemS 2023年第5期9卷 5731-5744页

作者： Xiao, Yuhao Yao, Yiping Chen, Kai Tang, Wenjie Zhu, Feng Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China

Predicting the computing resources required by simulation applications can provide a more reasonable resource-allocation scheme for efficient execution. Existing prediction methods based on machine learning, such as classification/regression, typically must accurately predict the runtime of simulation applications and select the optimal computing resource allocation scheme by sorting the length of the simulation runtime. However, the ranking results are easily affected by the simulation runtime prediction accuracy. This study proposes a time-sensitive learning-to-rank (LTR) approach for cloud simulations resource prediction. First, we use the Shapley additive explanation (SHAP) value from the field of explainable artificial intelligence (XAI) to analyze the impact of relevant factors on the simulation runtime and to extract the feature dimensions that significantly affect the simulation runtime. Second, by modifying the target loss function of the rankboost algorithm and training a time-sensitive LTR model based on simulation features, we can accurately predict the computing resource allocation scheme that maximizes the execution efficiency of simulation applications. Compared with the traditional machine learning prediction algorithm, the proposed method can improve the average sorting performance by 3%-48% and can accurately predict the computing resources required for the simulation applications to execute in the shortest amount of time.

关键词： Cloud computing complex system simulation Computing resource prediction Learning to rank

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Research on Determination Method of Model Granularity in complex system simulation

Research on Determination Method of Model Granularity in Com...

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32nd Chinese Control Conference (CCC)

作者： Liu Fu-xian Yang Lin Zhao Lin-feng Xing Qua-hua Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China

ISBN: (纸本)9781479900305

The determination method of model granularity is a main problem in complex system simulation. Firstly, a formalized describing method of simulation model is given on the base of analyzing the characteristic of complex system. Then the concepts of attainability, feasibility, logic accuracy, experimental framework of the model are introduced, and the formalization definition of model validity and simulation granularity are given. Finally, rules of determining the simulation granularity are proposed.

关键词： complex system simulation Model granularity Experiment framework Formalized describing

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simulation Runtime Prediction Approach based on Stacking Ensemble Learning 11

Simulation Runtime Prediction Approach based on Stacking Ens...

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11th International Conference on simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH)

作者： Xiao, Yuhao Yao, Yiping Zhu, Feng Chen, Kai Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China

ISBN: (纸本)9789897585289

Many technologies in complex system simulation (CSM), such as resource scheduling and load balancing, largely rely on historical data with different characteristics to predict the future. The accuracy of runtime prediction has a significant impact on scheduling performance. However, with cloud computing becoming the main infrastructure for deploying CSM applications, the current prediction methods are difficult to adapt to the dynamic changes of cloud computing resources. Insufficient computing resource allocation will be difficult to support the efficient operation of simulation. In addition, excessive computing resource allocation will lead to higher computing and data communication costs. Therefore, a simulation runtime prediction approach based on stacking ensemble learning has been proposed, which uses the characteristic variables of simulation applications (such as the number of simulation entities, the number of simulation events, the simulation time, etc.) and the performance monitoring data of computing resources (such as CPU utilization, memory utilization, etc.) as the characteristic inputs. The machine learning algorithms such as XBG, SVG, MLP are integrated by stacking model, and the performance of the integrated learning algorithm is comprehensively evaluated by mean absolute error (MAE), accuracy (ACC), root mean square error (RMSE) and coefficient of determination (R2). Experimental results show that the proposed algorithm improve the prediction accuracy by 3% - 24% when compared with existing machine learning prediction methods.

关键词： complex system simulation Cloud Computing Runtime Prediction Ensembling

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State-of-the-Art Development of complex systems and Their simulation Methods

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complex system Modeling and simulation 2021年第4期1卷 271-290页

作者： Yiming Tang Lin Li Xiaoping Liu School of Computer and Information and Engineering Research Center of Safety Critical Industry Measure and Control TechnologyMinistry of EducationHefei University of TechnologyHefei 230601China

The research on complex systems is different from that on general systems because the former must consider self-organization,emergence,uncertainty,predetermination,and *** an important method to transform the world,a simulation is one of the most important skills to discover complex *** this study,we provide a survey on complex systems and their simulation ***,the development history of complex system research is summarized from two main ***,the eight common characteristics of the most complex systems are ***,the simulation methods of complex systems are introduced in detail from four aspects,namely,meta-synthesis methods,complex networks,intelligent technologies,and other *** the overall point of view,intelligent technologies are the driving force,and complex networks are the advanced ***-synthesis methods are the integration strategy,and other methods are the *** addition,we show three complex system simulation examples:digital reactor simulation,simulation of a logistics system in the industrial site,and crowd evacuation *** examples show that a simulation is a useful means and an important method in complex system ***,the future development prospects for complex systems and their simulation methods are suggested.

关键词： systems science complex systems complexity complex system simulation

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A simulation task partition method based on cloud computing resource prediction using ensemble learning

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simulation MODELLING PRACTICE AND THEORY 2022年第0期119卷

作者： Xiao, Yuhao Yao, Yiping Chen, Kai Tang, Wenjie Zhu, Feng Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China

The calculation processing and interactions between complex system simulation entities in a cloud-computing environment exhibit dynamic change. However, parallelized task partitioning methods often ignore these dynamic characteristics, which makes it difficult to improve execution efficiency for complex system simulations. Therefore, we propose a two-stage simulation task partition method that computes the resource prediction information. In the first stage, the features of collected data are analyzed to select the optimal feature dimensions. A stacking ensemble learning method is used to predict the simulation runtime under the condition of allocating various computing resources, and the number of resources required for the shortest runtime is obtained by sorting the predicted results. In the second stage, a multi-weight graph structure is designed to represent the dynamic interaction of simulation entities, with the goal of minimizing the load imbalance on each computing node, as well as the maximum number of communications between nodes. Subsequently, the simulated annealing optimization algorithm is used to partition multiple weight graphs and map simulation tasks to resources. We compared our multi-weight graph partition method with the Metis and Chacos models. The experimental results demonstrate that, while all three methods can achieve the shortest runtime with the predicted number of computing resources, our algorithm can further reduce the runtime by up to 26%.

关键词： complex system simulation Cloud computing Resource prediction Ensemble learning Resource partitioning

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Risk Topology Construction Method of Flight Manipulation in Turbulent-Windshear Conditions Based on Multivariate Copula Theory

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IEEE ACCESS 2020年 8卷 127801-127817页

作者： Wang, Guozhi Xu, Haojun Pei, Binbin Xue, Yuan Duan, Xiaocong Air Force Engn Univ Aeronaut Engn Coll Xian 710038 Peoples R China

complex weather conditions, especially turbulent-windshear conditions, have severe effects on the landing safety of an aircraft. Based on a distributed human-machine-environment real-time simulation system, virtual landing simulations in turbulent-windshear conditions were carried out using the Monte Carlo method. By analyzing the simulation results, it was determined that the flight parameters that significantly affect the landing safety of an aircraft are the sideslip angle beta, descent height Delta H, and angle of attack alpha. Based on multivariate copula theory, the statistical characteristics of the extreme values of the flight parameters were analyzed, the unknown parameters in the distribution models were identified, and the goodness of fit was tested. The risk of landing in turbulent-windshear conditions was quantitatively evaluated. By applying the quantitative risk evaluation method proposed in this study, the risk topology of flight manipulation for landing in turbulent-windshear conditions was constructed using the pitch angle variation Delta theta, yaw angle variation Delta psi and flight distance L. The risk topology can not only greatly improve the situational awareness of the pilot but also provide comprehensive and intuitive guidance for removing aircraft from the impacts of wind-shear.

关键词： complex system simulation Monte Carlo method turbulent-windshear multivariate copula theory manipulation risk topology

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Dynamic Resource Prediction in Cloud Computing for complex system Simulatiuon: A Probabilistic Approach Using Stacking Ensemble Learning

Dynamic Resource Prediction in Cloud Computing for Complex S...

引用

International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI)

作者： Wang, Shuai Yao, Yiping Xiao, Yuhao Chen, Huilong Natl Univ Def Technol Sch Syst Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781665423168

Dynamic resource prediction in cloud computing can provide support for allocating resources required by complex system simulation (CSS) on demand, which can improve the simulation performance and resource utilization. However, the resource requirements have strong volatility because of the dynamic changes of simulation entities in the CSS applications, and few limited dynamic prediction models can predict the resource with strong volatility. In this study, a probabilistic approach using stacking ensemble learning, which integrates random forest, long short-term memory networks, linear regression, and Gaussian process regression, is proposed to predict the cloud resources required by the CSS applications. The proposed approach can quantify the uncertainty information in the cloud resource prediction. Experiments show that the proposed probabilistic approach using stacking ensemble learning can achieve better performance compared with other resource prediction approaches.

关键词： component complex system simulation cloud computing dynamic resource prediction probabilistic approach stacking ensemble learning

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