A Prediction-Based Multi-Objective VM Consolidation Approach for Cloud Data Centers

Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource utilization.This paper proposes a prediction-basedmulti-objective VMconsolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical value.We use a hybrid model based on Auto-Regressive Integrated Moving Average(ARIMA)and Support Vector Regression(SVR)(HPAS)as a prediction model and consolidate VMs to PMs based on prediction results by HPAS,aiming at minimizing the total EC,performance degradation(PD),migration cost(MC)and resource wastage(RW)simultaneously.Experimental results usingMicrosoft Azure trace show the proposed approach has better prediction accuracy and overcomes the multi-objective consolidation approach without prediction(i.e.,Non-dominated sorting genetic algorithm 2,Nsga2)and the renowned Overload Host Detection(OHD)approaches without prediction,such as Linear Regression(LR),Median Absolute Deviation(MAD)and Inter-Quartile Range(IQR).

Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

Virtual machine(VM)consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers.Most existing studies have considered VM consolidation as a bin-packing problem,but the current schemes commonly ignore the long-term relationship between VMs and hosts.In 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 consumption.To 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.The 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 prediction.Then,based on the multi-step prediction,an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance.During the VM consolidation,the affinity value is used to select the migration VM and placement host.The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform.Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption,VM migration costs,and service level agreement(SLA)violations.

Energy-efficient virtual machine consolidation algorithm in cloud data centers

Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine(VM) consolidation algorithm named PVDE(prediction-based VM deployment algorithm for energy efficiency) is presented. The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposed algorithms. Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement(SLA) violations.

Energy-Efficient Framework for Virtual Machine Consolidation in Cloud Data Centers

With the advent of the era of cloud computing, the high energy consumption of cloud computing data centers has become a prominent problem, and how to reduce the energy consumption of cloud computing data center and improve the efficiency of data center has become the research focus of researchers all the world. In a cloud environment, virtual machine consolidation(VMC) is an effective strategy that can improve the energy efficiency. However, at the same time, in the process of virtual machine consolidation, we need to deal with the tradeoff between energy consumption and excellent service performance to meet service level agreement(SLA). In this paper, we propose a new virtual machine consolidation framework for achieving better energy efficiency-Improved Underloaded Decision(IUD) algorithm and Minimum Average Utilization Difference(MAUD) algorithm. Finally, based on real workload data on Planet Lab, experiments have been done with the cloud simulation platform Cloud Sim. The experimental result shows that the proposed algorithm can reduce the energy consumption and SLA violation of data centers compared with existing algorithms, improving the energy efficiency of data centers.

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Live Virtual Machine(VM)migration is one of the foremost techniques for progressing Cloud Data Centers’(CDC)proficiency as it leads to better resource usage.The workload of CDC is often dynamic in nature,it is better to envisage the upcoming workload for early detection of overload status,underload status and to trigger the migration at an appropriate point wherein enough number of resources are available.Though various statistical and machine learning approaches are widely applied for resource usage prediction,they often failed to handle the increase of non-linear CDC data.To overcome this issue,a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory(CDB-LSTM)model is proposed.The CDB-LSTM adopts Helly property of Hypergraph and Savitzky–Golay(SG)filter to select informative samples and exclude noisy inference&outliers.The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computa-tional complexity during live VM migration.Further,the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration.A Hypergraph based CDB-LSTM was vali-dated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.

Identification of navigation characteristics of single otter trawl vessel using four machine learning models

Fishing logbook records the fishing behaviors and other information of fishing vessels.However,the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment.The fishing vessel monitoring system(VMS)can monitor and record the navigation information of fishing vessels in real time,and it may be used to improve the accuracy of identifying the state of fishing vessels.If the VMS data and fishing logbook are combined to establish their relationships,then the navigation characteristics and fishing behavior of fishing vessels can be more accurately identified.Therefore,first,a method for determining the state of VMS data points using fishing log data was proposed.Secondly,the relationship between VMS data and the different states of fishing vessels was further explored.Thirdly,the state of the fishing vessel was predicted using VMS data by building machine learning models.The speed,heading,longitude,latitude,and time as features from the VMS data were extracted by matching the VMS and logbook data of three single otter trawl vessels from September 2012 to January 2013,and four machine learning models were established,i.e.,Random Forest(RF),Adaptive Boosting(AdaBoost),K-Nearest Neighbor(KNN),and Gradient Boosting Decision Tree(GBDT)to predict the behavior of fishing vessels.The prediction performances of the models were evaluated by using normalized confusion matrix and receiver operator characteristic curve.Results show that the importance rankings of spatial(longitude and latitude)and time features were higher than those of speed and heading.The prediction performances of the RF and AdaBoost models were higher than those of the KNN and GBDT models.RF model showed the highest prediction performance for fishing state.Meanwhile,AdaBoost model exhibited the highest prediction performance for non-fishing state.This study offered a technical basis for judging the navigation characteristics of fishing vessels,which improved the algorithm for judging the behavior of fishing vessels based on VMS data,enhanced the prediction accuracy,and upgraded the fishery management being more scientific and efficient.

改进黑猩猩算法的光伏发电功率短期预测

针对晴空、非晴空条件下光伏出力预测精度不高等问题,提出一种改进K均值(K-means++)算法和黑猩猩优化算法CHOA(chimpanzee optimization algorithm)相结合,优化最小二乘支持向量机LSSVM(least squares support vector machine)的模型,进行光伏功率预测。首先,利用密度聚类和混合评价函数改进K-means++对原始数据进行自适应类别划分。其次,通过相关性分析和随机森林特征提取构建模型的输入特征集。最后,根据特征集建立基于DK-PCHOA-LSSVM的短期光伏发电预测模型。结合实际算例,结果表明:该模型在恶劣天气下预测精度明显优于其他模型,验证了其有效性和优越性。

基于WCFSE-FSVM的转子振动故障诊断方法

为了提高含有噪声和野值的转子振动故障样本诊断精度,提出了基于WCFSE-FSVM的故障诊断方法。充分融合小波相关特征尺度熵(WCFSE)特征提取方法和FSVM故障诊断方法的优点,建立WCFSE-FSVM故障诊断模型。基于转子实验台模拟4种典型故障,获得原始故障数据;并利用WCFSE方法提取这些故障数据的WCFSE值,选取故障信号高频段中的尺度1和尺度2上的小波相关特征尺度熵W1和W2构造出振动信号的故障向量作为故障样本,建立FSVM诊断模型。实例分析显示:WCFSE-FSVM方法的转子故障诊断精度最高,即故障类别诊断精度为94.49%,故障严重程度的诊断精度为95.58%,二者都优于其它故障诊断方法。验证了WCFSEFSVM方法的可行性和有效性。

基于MasterCAM9.1的VM-32SA立式加工中心后置处理优化设计与实现研究

以VM-32SA加工中心四轴机床的NC程序的要求为研究对象,重点阐述了对MasterCAM9.1自带后处理文件进行修改、优化的关键技术,制定出符合VM-32SA机床需求的后置处理文件。以搓接鼓实际加工过程为例,检验后置出来NC程序的正确性。实践结果表明:加工过程没有出现报警,而且加工的零件能满足规定的精度要求,从而验证四轴后置文件的正确性,对其他控制系统机床的后置修改有一定的参考作用。

Hybrid Prairie Dog and Beluga Whale Optimization Algorithm for Multi-Objective Load Balanced-Task Scheduling in Cloud Computing Environments

The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.

机械掘进技术在弱胶结软岩巷道中的应用实践

弱胶结软岩强度低、胶结差、易风化、遇水易泥化,采用传统钻爆法掘进时技术难度大、安全风险高、效率低、成本高。基于此,提出以悬臂式掘进机机械掘进代替传统钻爆法掘进的技术方案,并开展悬臂式掘进机机械掘工业试验。工业试验时间长达309 d、累计掘进进尺1 297.21 m、总工程量27 729.49 m^(3),悬臂式掘进机纯截割能力19.15 m^(3)/h,平均每日进尺4.2 m,平均每班进尺3.41 m。结果证明,采用悬臂式掘进机机械掘进技术可以有效解决弱胶结软岩岩体内钻爆法掘进过程中遇到的生产难题,机械掘进效率可以达到钻爆法掘进效率的1.4倍以上,并在大幅度提高掘进效率的基础上降低了掘进成本。研究成果可为弱胶结软岩巷道掘进提供技术参考。

一种全自动沙漠植树机机械结构设计

针对现有沙漠植树机或辅助植树装置,存在操作模块多、效率较低等问题,在此提出研发一种全自动沙漠植树机,该植树机主要由机体框架、挖坑机构、植苗机构、覆土机构、浇水装置以及电控系统等组成。经样机制作实地种植实践检验证明,该植树机能可靠完成挖坑、投苗、覆土到浇水等连续植树工作,并实现了小树苗适量定点投放,具有自动化水平高、效率高、降低劳动强度等优点。

Windows 95虚拟机(VM)机制分析

本文从各个角度对Windows95的虚拟机机制进行了详尽的分析

Task scheduling and virtual machine allocation policy in cloud computing environment

Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.

A novel virtual machine deployment algorithm with energy efficiency in cloud computing

In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.

液氦温区VM-PT制冷机气量分配特性

VM气耦合脉冲管制冷机(VM-PT)是一种新型的液氦温区制冷机,为探索两级气耦合复杂的机理,本文采用Sage软件构建了低温调相VM-PT制冷机的整机模拟程序,研究了运行频率、平均压力、毛细管长度以及Er3Ni填充长度等参数对两级气量分配的影响。结果表明:运行频率、平均圧力、毛细管长度以及Er3Ni填充长度均会影响两级质量流的分配,进而影响制冷机的最低温度,权衡工质的做工能力以及蓄冷器损失两方面因素,该四个参数均存在一个最佳值。搭建了实验平台并对数值模拟进行了验证。在实验中通过优化毛细管和蓄冷器,在运行频率1.6 Hz、平均压力1.4 MPa、压比1.6的情况下得到了3.86 K的无负荷制冷温度,在4.2 K可提供约10 m W的制冷量。

多DSP局部总线与VME总线的接口设计

在多DSP信号处理系统的设计过程中,开发基于标准总线的信号处理模板已经成主流设计方案。这种设计方案的难点就是局部总线到标准总线的时序转换比较复杂。在详细介绍VME总线功能特点的基础上,给出了一种在FPGA控制下实现的工业控制计算机通过VME总线与多DSP信号处理板局部总线进行通信的接口设计方案。FPGA的控制功能采用状态机工作方式实现。

基于Oracle VM模板的Oracle RAC快速部署研究

Oracle RAC是Oracle私有云架构的关键组成部分,但是部署Oracle RAC除了需要安装Oracle Grid集群基础架构,针对操作系统、共享磁盘进行参数配置外,还需要进行繁琐的系统依赖包的安装及打补丁等,往往耗时10多个小时;针对部署Oracle RAC的复杂性,提出基于Oracle VM模板部署RAC的实践方法; Oracle VM模板提供了一种通过提供预安装和预配置的软件映像来部署完全配置的软件体系的创新方法,可消除安装和配置成本;项目实践表明,利用Oracle VM模板能够在1 h内部署完毕Oracle RAC,极大地提高了部署效率及成功率;基于Oracle VM模板部署RAC的稳定性需要进一步在实际生产环境中检验,方法非常适合在高校教学环境中使用。

Allocation and Migration of Virtual Machines Using Machine Learning

Cloud computing promises the advent of a new era of service boosted by means of virtualization technology.The process of virtualization means creation of virtual infrastructure,devices,servers and computing resources needed to deploy an application smoothly.This extensively practiced technology involves selecting an efficient Virtual Machine(VM)to complete the task by transferring applications from Physical Machines(PM)to VM or from VM to VM.The whole process is very challenging not only in terms of computation but also in terms of energy and memory.This research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data centres.Machine Learning(ML)based Artificial Bee Colony(ABC)is used to rank the VM with respect to the load while considering the energy efficiency as a crucial parameter.The most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy,applications are migrated fromoneVMto another.The simulation analysis is performed inMatlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.