The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it...The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it streamed the scenes as video series to play through network. Therefore, cloud gaming is a capable approach, which quickly increases the cloud computing platform. Obtaining enhanced user experience in cloud gaming structure is not insignificant task because user anticipates less response delay and high quality videos. To achieve this, cloud providers need to be able to accurately predict irregular player workloads in order to schedule the necessary resources. In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming. The workload of each resource is computed based on developed Fractional Rider Deep LSTM network. Moreover, resource allocation is performed by fractional Rider-based Harmony Search Algorithm (Rider-based HSA). This Fractional Rider-based HSA is developed by combining Fractional calculus (FC), Rider optimization algorithm (ROA) and Harmony search algorithm (HSA). Moreover, the developed Fractional Rider Deep LSTM is developed by integrating FC and Rider Deep LSTM. In addition, the multi-objective parameters, namely gaming experience loss QE, Mean Opinion Score (MOS), Fairness, energy, network parameters, and predictive load are considered for efficient resource allocation and workload prediction. Additionally, the developed workload prediction model achieved better performance using various parameters, like fairness, MOS, QE, energy and delay. Hence, the developed Fractional Rider Deep LSTM model showed enhanced results with maximum fairness, MOS, QE of 0.999, 0.921, 0.999 and less energy and delay of 0.322 and 0.456.展开更多
With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,prov...With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.展开更多
With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’infrastructures.Subsequently,recent approaches a...With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’infrastructures.Subsequently,recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources,where the energy consumption and the operational costs are minimized.However,to make better cost decisions in these strategies,the performance and energy awareness should be supported at both Physical Machine(PM)and Virtual Machine(VM)levels.Therefore,in this paper,a novel hybrid approach is proposed,which jointly considered the prediction of performance variation,energy consumption and cost of heterogeneous VMs.This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance,in which the power consumption and resource usage are utilized for estimating the VMs’total cost.Specifically,the service performance variation is handled by detecting the underloaded and overloaded PMs;thereby,the decision(s)is made in a cost-effective manner.Detailed testbed evaluation demonstrates that the proposed approach not only predicts the VMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation,with a high prediction accuracy on the basis of historical workload patterns.展开更多
负载预测的精度是影响云平台弹性资源管理的主要因素之一。而云平台中存在着大量的短任务负载序列,其历史信息不足和不平滑的特性导致难以选择合适的模型进行精准预测。对此提出了一种领域对抗自适应的短任务负载预测模型。该模型采用...负载预测的精度是影响云平台弹性资源管理的主要因素之一。而云平台中存在着大量的短任务负载序列,其历史信息不足和不平滑的特性导致难以选择合适的模型进行精准预测。对此提出了一种领域对抗自适应的短任务负载预测模型。该模型采用奇异谱分析(singular spectrum analysis,SSA)对样本进行平滑处理;联合第四版本的Mueen相似度搜索算法(the fourth version of Mueen’s algorithm for similarity search,MASS_V4)与时间特征进行域间相似性计算,获得合适的源域数据来辅助迁移预测;将门控循环单元(gated recurrent unit,GRU)作为基准器构建网络,并利用Y差异定义新的损失函数,通过对抗过程建立出表征能力强的短任务负载预测模型。将所提方法在两个真实的云平台数据集上与其他常用的云负载预测算法对比,均表现出较高的预测精度。展开更多
脑力负荷是人机系统中人的绩效的一个重要因素。对飞行员脑力负荷展开研究,为飞机驾驶舱设计及其仪表设备的符合性验证提供参考。通过实验得到生理测量、绩效测量、主观测量的各项指标。利用单因素方差分析法提取对飞行员脑力负荷的敏...脑力负荷是人机系统中人的绩效的一个重要因素。对飞行员脑力负荷展开研究,为飞机驾驶舱设计及其仪表设备的符合性验证提供参考。通过实验得到生理测量、绩效测量、主观测量的各项指标。利用单因素方差分析法提取对飞行员脑力负荷的敏感指标。结果表明:注视频率、注视总时间、眨眼率、平均瞳孔直径变化率、NASA_TLX(NASA task load index)、正确率的主效应显著(P<0.05)。采用自组织算法GMDH(group method of data handling)与线性回归的结合方法,建立飞行员脑力负荷预测模型;并且得到模型拟合度为85.47%。因此,GMDH与线性回归的结合方法可以较好地预测飞行员脑力负荷。展开更多
飞行员工作负荷是影响飞机运行安全的重要因素。开展飞行员工作负荷预测是适航审定过程中,验证驾驶舱设计是否符合适航规章的重要手段。本文针对某型民用飞机设计了模拟飞行试验,用于采集飞行员生理指标数据和国家航空航天局任务负荷指...飞行员工作负荷是影响飞机运行安全的重要因素。开展飞行员工作负荷预测是适航审定过程中,验证驾驶舱设计是否符合适航规章的重要手段。本文针对某型民用飞机设计了模拟飞行试验,用于采集飞行员生理指标数据和国家航空航天局任务负荷指数(National Aeronautics and Space Administration task lood index,NASA-TLX)量表评价数据。以飞行员生理指标数据为输入,NASA-TLX量表主观评价数据为输出,建立了基于粒子群算法优化的支持向量回归机(Particle swarm optimization-support vector regression,PSO-SVR)模型的飞行员工作负荷预测模型。对本文建立的PSO-SVR模型与默认参数的支持向量回归机(Support vector regression,SVR)模型的预测精度进行了对比,针对4个不同场景,预测精度分别提高了7.5%、9.5%、7%和5.8%,结果表明基于PSO-SVR的预测模型得到的飞行员工作负荷预测值精度更高。展开更多
文摘The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it streamed the scenes as video series to play through network. Therefore, cloud gaming is a capable approach, which quickly increases the cloud computing platform. Obtaining enhanced user experience in cloud gaming structure is not insignificant task because user anticipates less response delay and high quality videos. To achieve this, cloud providers need to be able to accurately predict irregular player workloads in order to schedule the necessary resources. In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming. The workload of each resource is computed based on developed Fractional Rider Deep LSTM network. Moreover, resource allocation is performed by fractional Rider-based Harmony Search Algorithm (Rider-based HSA). This Fractional Rider-based HSA is developed by combining Fractional calculus (FC), Rider optimization algorithm (ROA) and Harmony search algorithm (HSA). Moreover, the developed Fractional Rider Deep LSTM is developed by integrating FC and Rider Deep LSTM. In addition, the multi-objective parameters, namely gaming experience loss QE, Mean Opinion Score (MOS), Fairness, energy, network parameters, and predictive load are considered for efficient resource allocation and workload prediction. Additionally, the developed workload prediction model achieved better performance using various parameters, like fairness, MOS, QE, energy and delay. Hence, the developed Fractional Rider Deep LSTM model showed enhanced results with maximum fairness, MOS, QE of 0.999, 0.921, 0.999 and less energy and delay of 0.322 and 0.456.
文摘With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.
文摘With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’infrastructures.Subsequently,recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources,where the energy consumption and the operational costs are minimized.However,to make better cost decisions in these strategies,the performance and energy awareness should be supported at both Physical Machine(PM)and Virtual Machine(VM)levels.Therefore,in this paper,a novel hybrid approach is proposed,which jointly considered the prediction of performance variation,energy consumption and cost of heterogeneous VMs.This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance,in which the power consumption and resource usage are utilized for estimating the VMs’total cost.Specifically,the service performance variation is handled by detecting the underloaded and overloaded PMs;thereby,the decision(s)is made in a cost-effective manner.Detailed testbed evaluation demonstrates that the proposed approach not only predicts the VMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation,with a high prediction accuracy on the basis of historical workload patterns.
文摘负载预测的精度是影响云平台弹性资源管理的主要因素之一。而云平台中存在着大量的短任务负载序列,其历史信息不足和不平滑的特性导致难以选择合适的模型进行精准预测。对此提出了一种领域对抗自适应的短任务负载预测模型。该模型采用奇异谱分析(singular spectrum analysis,SSA)对样本进行平滑处理;联合第四版本的Mueen相似度搜索算法(the fourth version of Mueen’s algorithm for similarity search,MASS_V4)与时间特征进行域间相似性计算,获得合适的源域数据来辅助迁移预测;将门控循环单元(gated recurrent unit,GRU)作为基准器构建网络,并利用Y差异定义新的损失函数,通过对抗过程建立出表征能力强的短任务负载预测模型。将所提方法在两个真实的云平台数据集上与其他常用的云负载预测算法对比,均表现出较高的预测精度。
文摘脑力负荷是人机系统中人的绩效的一个重要因素。对飞行员脑力负荷展开研究,为飞机驾驶舱设计及其仪表设备的符合性验证提供参考。通过实验得到生理测量、绩效测量、主观测量的各项指标。利用单因素方差分析法提取对飞行员脑力负荷的敏感指标。结果表明:注视频率、注视总时间、眨眼率、平均瞳孔直径变化率、NASA_TLX(NASA task load index)、正确率的主效应显著(P<0.05)。采用自组织算法GMDH(group method of data handling)与线性回归的结合方法,建立飞行员脑力负荷预测模型;并且得到模型拟合度为85.47%。因此,GMDH与线性回归的结合方法可以较好地预测飞行员脑力负荷。
文摘飞行员工作负荷是影响飞机运行安全的重要因素。开展飞行员工作负荷预测是适航审定过程中,验证驾驶舱设计是否符合适航规章的重要手段。本文针对某型民用飞机设计了模拟飞行试验,用于采集飞行员生理指标数据和国家航空航天局任务负荷指数(National Aeronautics and Space Administration task lood index,NASA-TLX)量表评价数据。以飞行员生理指标数据为输入,NASA-TLX量表主观评价数据为输出,建立了基于粒子群算法优化的支持向量回归机(Particle swarm optimization-support vector regression,PSO-SVR)模型的飞行员工作负荷预测模型。对本文建立的PSO-SVR模型与默认参数的支持向量回归机(Support vector regression,SVR)模型的预测精度进行了对比,针对4个不同场景,预测精度分别提高了7.5%、9.5%、7%和5.8%,结果表明基于PSO-SVR的预测模型得到的飞行员工作负荷预测值精度更高。
