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Virtual Machine Consolidation with multi-step prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

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Computers, Materials & Continua 2023年第7期76卷 81-105页

作者： Pingping Li Jiuxin Cao School of Cyber Science and Engineering Southeast UniversityNanjing211189China

Virtual machine(VM)consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data *** existing studies have considered VM consolidation as a bin-packing problem,but the current schemes commonly ignore the long-term relationship between VMs and *** addition,there is a lack of long-term consideration for resource optimization in the VM consolidation,which results in unnecessary VM migration and increased energy *** address these limitations,a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers(MPaAF-VMC)is *** proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs,and obtains the stage demand of resources in the future period through multi-step prediction,which is realized by iterative ***,based on the multi-step prediction,an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean *** the VM consolidation,the affinity value is used to select the migration VM and placement *** proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation *** results show that the proposed method can achieve significant improvement in reducing energy consumption,VM migration costs,and service level agreement(SLA)violations.

关键词： Cloud computing VM consolidation multi-step prediction affinity relationship energy efficiency

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A Content-Aware Bitrate Selection Method Using multi-step prediction for 360-Degree Video Streaming

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ZTE Communications 2022年第4期20卷 96-109页

作者： GAO Nianzhen YU Yifang HUA Xinhai FENG Fangzheng JIANG Tao Huazhong University of Science and Technology Wuhan 430074China ZTE Corporation Shenzhen 518057China

A content-aware multi-step prediction control(CAMPC)algorithm is proposed to determine the bitrate of 360-degree videos,aim⁃ing to enhance the quality of experience(QoE)of users and reduce the cost of video content providers(VCP).The CAMPC algorithm first em⁃ploys a neural network to generate the content richness and combines it with the current field of view(FOV)to accurately predict the probability distribution of tiles being ***,for the tiles in the predicted viewport which directly affect QoE,the CAMPC algorithm utilizes a multi-step prediction for future system states,and accordingly selects the bitrates of multiple subsequent steps,instead of an instantaneous ***,it controls the buffer occupancy to eliminate the impact of prediction *** implement CAMPC on players by building a 360-degree video streaming platform and evaluating other advanced adaptive bitrate(ABR)rules through the real *** results show that CAMPC can save 83.5%of bandwidth resources compared with the scheme that completely transmits the tiles outside the viewport with the Dynamic Adaptive Streaming over HTTP(DASH)***,the proposed method can improve the system utility by 62.7%and 27.6%compared with the DASH official and viewport-based rules,respectively.

关键词： DASH content-aware FOV prediction bitrate adaptation multi-step prediction generalized predictive control

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Research on multi-step prediction of Inlet NOx Concentration Based on VMD-ARIMA Model

Research on Multi-step Prediction of Inlet NOx Concentration...

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第40届中国控制会议

作者： Zheng Zhao Yijie Ma Automation Department of North China Electric Power University

In view of the large hysteresis in the measurement of the inlet NOx concentration,simply using single-step prediction method cannot solve this problem *** order to better solve the problem of measurement lag and improve control quality,a multi-step prediction model based on variational modal decomposition-autoregressive differential moving average model（VMD-ARIMA） is proposed,which is convenient for the system to control the amount of ammonia injection online and reduce NOx ***,the appropriate number of decomposition levels for the inlet NOx time series is selected,and then VMD decomposition is ***,use the order criterion to determine the order of each layer component,and then use the ARIMA model to make multi-step predictions,and keep the model updated ***,the multi-step prediction values of each layer are superimposed correspondingly to obtain the final multi-step prediction value *** results show that:First,compared to the Back Propagation Neural Network（BP） model and Least Square Support Vector Machine（LSSVM） model,the VMD-ARIMA model has simpler inputs and can predict the inlet NOx concentration more ***,compared with the ARIMA model,the VMD-ARIMA model has better multi-step prediction performance,higher prediction accuracy,and can predict the amount of NOx generated one minute in advance.

关键词： NOx Production Variational Modal Decomposition Autoregressive Differential Moving Average Model multi-step prediction

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Video Frame prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

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Computers, Materials & Continua 2023年第5期75卷 2615-2639页

作者： Navin Ranjan Sovit Bhandari Yeong-Chan Kim Hoon Kim Iot and Big Data Research Center Incheon National UniversityYeonsu-guIncheon22012Korea 2Department of Electronics EngineeringIncheon National UniversityYeonsu-guIncheon22012Korea

Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame *** is one of the crucial issues in computer vision and has many real-world applications,mainly focused on predicting future scenarios to avoid undesirable ***,modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene,such as occlusions,camera movements,delay and *** frame synthesis or optical-flow estimation are common approaches used by ***,researchers mainly focused on video prediction using one of the *** methods have limitations,such as direct frame synthesis,usually face blurry prediction due to complex pixel distributions in the scene,and optical-flow estimation,usually produce artifacts due to large object displacements or obstructions in the *** this paper,we constructed a deep neural network Frame prediction Network(FPNet-OF)with multiplebranch inputs(optical flow and original frame)to predict the future video frame by adaptively fusing the future object-motion with the future frame *** key idea is to jointly optimize direct RGB frame synthesis and dense optical flow estimation to generate a superior video prediction *** various real-world datasets,we experimentally verify that our proposed framework can produce high-level video frame compared to other state-ofthe-art framework.

关键词： Video frame prediction multi-step prediction optical-flow prediction delay deep learning

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prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

Prediction Study on Anti-Slide Control of Railway Vehicle Ba...

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2012 International Conference on Solid State Devices and Materials Science(SSDMS 2012)

作者： Lijun Yang Jimin Zhang College of Automotive Engineering Tongji University Railway &Urban Mass Transit Research Institute Tongji University

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks’ hidden layer computed with K-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

关键词： Railway vehicle Brake anti-slide control RBF neural network K-means clustering algorithm multi-step prediction

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Generalized Predictive Control of Time-Delay Nonlinear Systems Based on Extreme Learning Machine

Generalized Predictive Control of Time-Delay Nonlinear Syste...

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第30届中国控制与决策会议

作者： Li Muwei Zhou Ying Wu Qiang Nanjing University of Posts and Telecommunications College of Automation

For a class of nonlinear controlled objects with time-delay,this paper proposes a generalized predictive self-tuning control method based on extreme learning *** the generalized predictive self-tuning control（GPC）,the predictive model of the nonlinear controlled object is established by the extreme learning machine（ELM）,and constantly revising forecast output data to improve the accuracy of the *** controller adopts a GPC implicit correction algorithm,without to identify the model parameters,the calculated amount is greatly *** simulation shows that the method in this paper is superior and practical,the prediction output track the reference trajectory better than the commonly used PID self-tuning method.

关键词： nonlinear time-delay generalized predictive control extreme learning machine multi-step prediction

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