基于BP神经网络的气象云资源调度系统

人工调度气象云资源会造成资源浪费。本文设计一种基于BP神经网络的气象云资源调度系统。该系统使用BP神经网络学习虚拟机负载历史数据,并对其进行预测;基于预测的虚拟机负载,设计一种面向多类资源的虚拟机非增排序策略;使用首次适应算法对排序后的虚拟机进行云资源调度。该系统在江西省气象云平台中进行了实验和功能验证。     

基于随机模型的云平台资源调度策略设计

针对云计算资源管理的实际需求,提出一种基于随机模型的云平台调度策略,设计合理高效的资源调度算法,解决传统代数模型请求丢失率高以及其他随机模型负载均衡指标性能较差的问题,从而在服务性能和执行效率的基础上保证服务器的资源负载,使云平台处于相对稳定的状态。在实验环境中的验证结果表明,该调度策略能够优化虚拟资源的使用效率和服务响应时间,同时能够达到较好的负载均衡并降低运营成本。     

基于多智能体强化学习的微服务弹性伸缩方法

针对现有微服务水平扩展策略难以应对异构应用对多种资源的差异化需求问题,提出了一种基于多智能体强化学习的微服务弹性伸缩方法。首先,通过刻画微服运行状态、资源调整动作及收益等要素建模云应用资源调整问题;其次,基于深度神经网络训练策略网络以决策资源调整操作,训练价值网络以评价决策优劣并优化调整策略;最后,提出中心化模型训练与分布式资源调整动作相结合的微服务弹性伸缩策略。实验结果表明,该方法能够根据负载波动及时调整各微服务的资源分配量,有效减少了云应用请求响应时间,并降低了云平台的资源使用成本。     

一种BP神经网络和遗传算法混合的云平台负载 预测方法

在云计算提供高效,便捷等强大服务的背后,是日益攀升的能耗问题。准确的预测云平台的负载(如CPU,内存的使用)在任务调度,云能效方面具有重要意义。在以往研究中,线性自回归算法在预测请求资源的粒度上存在不足,本文提出一种基于BP神经网络与遗传算法混合的负载预测方法,结合遗传算法良好的全局搜索能力与神经网络强大的非线性拟合能力,建立CPU资源的请求预测模型。实验通过Google的云平台数据作为训练,测试集。实验结果表明该方法有效的预测了CPU资源请求量,进而可以在此基础上调整服务资源,实现绿色调度。     

基于Docker的云资源弹性调度策略

针对云资源弹性调度问题,结合Ceph数据存储的特点,提出一种基于Docker容器的云资源弹性调度策略。首先,指出Docker容器数据卷不能跨主机的特性给应用在线迁移带来了困难,并对Ceph集群的数据存储方法进行改进;然后,建立了一个基于节点综合负载的资源调度优化模型;最后,将Ceph集群和Docker容器的特点相结合,利用Docker Swarm实现了既考虑数据存储、又考虑集群负载的应用容器部署算法和应用在线迁移算法。实验结果表明,与一些调度策略相比,该调度策略对集群资源进行了更细粒度的划分,实现了云平台资源的弹性调度,并在保证应用性能的同时,达到了合理利用云平台资源和降低数据中心运营成本的目的。     

基于多策略粒子群优化RBF的云资源预测模型

针对云计算资源利用率低等问题,构建基于多策略粒子群优化RBF神经网络的云资源预测模型(MPSO-RBF)。采用改进的粒子群算法对RBF神经网络参数进行优化,避免随机初始化参数引起的预测精度低等问题;对于粒子群容易陷入局部最优解等问题,采用动态惯性权重、自适应学习因子和变异粒子位置3种策略对粒子群进行改进,提高算法的寻优能力。基于云计算资源负载数据,将该模型与BP、RBF和PSO-RBF模型进行对比实验,验证了该模型具有良好的性能。     

基于排队理论的容器云弹性伸缩策略研究

弹性伸缩技术是灾害应急云计算中心的关键技术之一，基于合理的伸缩策略调整服务单元数量，有效解决高并发场景下应用的稳定性和性能等问题。Kubernetes作为目前主流的容器云管理平台，内置的HPA弹性伸缩策略，可能存在着资源供应过度和供应不足等问题。针对这一问题，提出了一种基于Kubernetes的容器云弹性伸缩策略ESBQT，利用排队理论构建满足平均请求响应时间最小约束条件下的Pod服务单元数供给优化模型。实验测试表明，ESBQT策略面临大流量、高并发请求时，资源供给更加合理，有效保证了应用的性能和稳定性。     

基于Kubernetes云原生的弹性伸缩研究

随着云技术的不断发展和普及,为了更好地利用云平台的优点和特性,云原生应用服务不断涌现,如何利用云平台的特性来服务软件设计和开发成为了难题,例如如何利用云平台的弹性伸缩特性。云原生目前主流的容器编排技术Kubernetes支持自动伸缩,却存在一些需要针对具体情况进行优化改进的问题。本文主要针对使用Kubernetes编排的5G核心网网元PCF(Policy Control Function)的水平自动伸缩进行研究,通过基于自定义的负载数据(CPU使用率、内存使用率、交易量、带宽使用率)统计,根据历史负载数据使用LSTM来预测未来的负载,并设计了一种基于预测负载的可行的弹性伸缩算法,从而提出一种提前感知的、弹性的、不影响业务的弹性伸缩方法,并进行了大量的实验和统计,来论证方法的可行性和正确性。     

基于熵权层次分析法的云平台负载预测

为提高对云平台负载情况的预测精度,降低预测负载值的波动性,研究非线性时间序列的预测算法。对影响云平台运行的因素进行认知和分解,提出基于熵权层次分析法的主客观评价模型。在此基础上,建立基于差分整合移动平均自回归模型(autoregressive integrated moving average model,ARIMA)和BP(back propagation)神经网络的云平台负载预测模型。实验结果表明,基于熵权层次分析法的评价模型吸收主客观赋权法的优势,能够合理准确地反映云平台的实时负载情况;基于ARIMA-BP的预测模型得出的预测结果具有更小误差的精度和波动较小的数值稳定性。     

云环境下基于监督学习的资源调度策略研究与实现

在云环境下,任务执行节点是异构和动态的,而且资源节点的利用率和负载的均衡很大程度上会影响云服务端的执行效率,如何为用户的任务调度合适的资源节点是关键。论文提出了一种基于监督学习的资源调度策略,并给出了该策略的整体框架,系统调度预测模型和算法描述,初步测算表明,该算法具有较好的适应性。     

一种云环境下的主机负载预测方法

云计算是一种基于信息网络的计算模式和服务模式,它将信息技术资源以服务方式动态、弹性地提供给用户,使用户可以按需使用。由于受到主机的启动时间、资源分配时间以及任务调度时间等因素的影响,在云环境下提供给用户的服务存在时延问题。因此,工作负载预测是云环境下一种重要的能源优化的方式。此外,由于云中工作负载的变化具有十分大的波动性,因此增加了预测模型的预测难度。提出了一种基于自回归模型和Elman神经网络的预测模型(Hybrid Auto Regressive Moving Average model and Elman neural network,HARMA-E),其使用ARMA模型进行预测,再使用ENN模型对ARMA模型的误差进行预测,通过修正ARMA的输出值得到最终的预测值。仿真实验结果表明,该预测模型能够较好地提升主机负载预测值的准确度。     

基于组合预测模型的云计算资源负载预测研究

随着云计算技术的不断发展,云计算资源负载变化呈现出越来越复杂的特征。针对云计算资源的负载预测问题,综合考虑云计算环境中资源负载时间序列的线性与非线性特性,提出了一种基于自回归移动平均模型ARIMA与长短期记忆网络LSTM的组合预测模型LACL。使用公开数据集与传统负载预测模型进行了对比实验,实验结果表明,该云计算资源组合预测模型预测精度明显高于其他预测模型,显著 降低了云环境中对资源负载的实时预测误差。     

Elastic resource provisioning in hybrid mobile cloud for computationally intensive mobile applications

The paper addresses the integration of hybrid cloud with mobile applications. The challenge about hybrid mobile cloud resource provisioning is the trade-offs between energy consumption, performance provided to users and how resources, such as processing power and network, are being utilized. The proposed elastic hybrid mobile cloud resource provisioning model is jointly optimized to improve mobile user experience within the constraints of available resources and user QoS requirement. The paper presents the system utility of hybrid cloud system involving local cloud and public cloud infrastructure. From the perspectives of both mobile applications and cloud providers, the proposed system utility is optimized to improve the performance of mobile applications and the utilization of cloud resources. The proposed elastic hybrid mobile cloud resource provisioning algorithm includes two sub-algorithms. To evaluate and validate performance of the proposed algorithm, a series of experiments are conducted. The comparison results and analyses are discussed. The experimental results show the improvement to previous works.     

Real-time learning capability of neural networks

In some practical applications of neural networks, fast response to external events within an extremely short time is highly demanded and expected. However, the extensively used gradient-descent-based learning algorithms obviously cannot satisfy the real-time learning needs in many applications, especially for large-scale applications and/or when higher generalization performance is required. Based on Huang's constructive network model, this paper proposes a simple learning algorithm capable of real-time learning which can automatically select appropriate values of neural quantizers and analytically determine the parameters (weights and bias) of the network at one time only. The performance of the proposed algorithm has been systematically investigated on a large batch of benchmark real-world regression and classification problems. The experimental results demonstrate that our algorithm can not only produce good generalization performance but also have real-time learning and prediction capability. Thus, it may provide an alternative approach for the practical applications of neural networks where real-time learning and prediction implementation is required.     

A Spatial-Temporal Attention Model for Human Trajectory Prediction

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory (LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention (ST-Attention) model, which studies spatial and temporal affinities jointly. Specifically, we introduce an attention mechanism to extract temporal affinity, learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.      

基于LSTM网络的移动云计算多元负载预测模型

针对移动云主机负载变化大、难以精准预测的问题,提出一种联合特征选择下基于长短期记忆网络的AR-LSTM-ED负载预测模型,能够对云主机负载进行单步和长时间多步预测。首先采用联合特征选择的方法得到与目标预测负载序列相关的其他负载序列,并且利用适用于在线预测的无抽取的小波变换方法将目标预测特征分解成更加易于预测的子序列。最后将这些序列和目标预测序列一起输入AR-LSTM-ED模型中,AR-LSTM-ED模型利用长短期记忆编-解码网络对目标负载进行预测,具有能够捕捉负载中的长期依赖关系的优点,且进一步结合了自回归模型（AR）以预测负载中的线性数据。在真实的Google云计算数据集上验证算法,对比实验结果表明,本文提出的方法取得了更好的性能。     

A bipolar resource management framework for resource provisioning in Cloud’s virtualized environment

Cloud’s profitability is mainly driven by the business, and on the other hand, a successful business is hardly geared with clients’ satisfaction. Therefore, there is high competition between cloud providers for satisfying clients and attracting more of them. In this way, long term business success factors should also be considered in addition to short term profit factors regarded in conventional resource provisioning procedures. Conventional resource management approaches to achieve short term profit inevitably lead to job rejection and violation from response time based SLAs while short response time and low job rejection are of those important factors to clients’ satisfaction. Therefore, this paper proposes a novel bipolar resource management framework which results in preventing from job rejection and having considerably reduced violations from response time based SLAs as well as providing short term profits. The proposed framework uses a neural network based predictor and genetic algorithm for optimal resource management through live migration. It also employs a prediction based temporal infinite pool, called the temporal cloud, which regards job rejection prevention. The evaluation of the proposed framework demonstrates that it can provide short term profits, beside it prevents from job rejection and reduces response time violations considerably.     

An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach

With the recent advancements in Internet-based computing models, the usage of cloud-based applications to facilitate daily activities is significantly increasing and is expected to grow further. Since the submitted workloads by users to use the cloud-based applications are different in terms of quality of service (QoS) metrics, it requires the analysis and identification of these heterogeneous cloud workloads to provide an efficient resource provisioning solution as one of the challenging issues to be addressed. In this study, we present an efficient resource provisioning solution using metaheuristic-based clustering mechanism to analyze cloud workloads. The proposed workload clustering approach used a combination of the genetic algorithm and fuzzy C-means technique to find similar clusters according to the user’s QoS requirements. Then, we used a gray wolf optimizer technique to make an appropriate scaling decision to provide the cloud resources for serving of cloud workloads. Besides, we design an extended framework to show interaction between users, cloud providers, and resource provisioning broker in the workload clustering process. The simulation results obtained under real workloads indicate that the proposed approach is efficient in terms of CPU utilization, elasticity, and the response time compared with the other approaches.

     

基于长-短时序特征融合的资源负载预测模型

高准确率的资源负载预测能够为实时任务调度提供依据,从而降低能源消耗。但是,针对资源负载的时间序列的预测模型,大多是通过提取时间序列的长时序依赖特性来进行短期或者长期预测,忽略了时间序列中的短时序依赖特性。为了更好地对资源负载进行长期预测,提出了一种基于长-短时序特征融合的边缘计算资源负载预测模型。首先,利用格拉姆角场（GAF）将时间序列转变为图像格式数据,以便利用卷积神经网络（CNN）来提取特征;然后,通过卷积神经网络提取空间特征和短期数据的特征,用长短期记忆（LSTM）网络来提取时间序列的长时序依赖特征;最后,将所提取的长、短时序依赖特征通过双通道进行融合,从而实现长期资源负载预测。实验结果表明,所提出的模型在阿里云集群跟踪数据集CPU资源负载预测中的平均绝对误差（MAE）为3.823,均方根误差（RMSE）为5.274,拟合度（R²）为0.815 8,相较于单通道的CNN和LSTM模型、双通道CNN+LSTM和ConvLSTM+LSTM模型,以及资源负载预测模型LSTM-ED和XGBoost,所提模型的预测准确率更高。     

Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm

The real-world building can be regarded as a comprehensive energy engineering system; its actual energy consumption depends on complex affecting factors, including various weather data and time signature. Accurate energy consumption forecasting and effective energy system management play an essential part in improving building energy efficiency. The multi-source weather profile and energy consumption data could enable integrating data-driven models and evolutionary algorithms to achieve higher forecasting accuracy and robustness. The proposed building energy consumption forecasting system consists of three layers: data acquisition and storage layer, data pre-processing layer and data analytics layer. The core part of the data analytics layer is a hybrid genetic algorithm (GA) and long-short term memory (LSTM) neural network model for accurate and robust energy prediction. LSTM neural network is adopted to capture the interrelationship between energy consumption data and time. GA is adopted to select the optimal architecture for LSTM neural networks to improve its forecasting accuracy and robustness. The hyper-parameters for determining LSTM architecture include the number of LSTM layers, number of neurons in each LSTM layer, dropping rate of each LSTM layer and network learning rate. Meanwhile, the effects of historical weather profile and time horizon of past information are also investigated. Two real-life educational buildings are adopted to test the performance of the proposed building energy consumption forecasting system. Experiments reveal that the proposed adaptive LSTM neural network performs better than the existing feedforward neural network and LSTM-based prediction models in accuracy and robustness. It also outperforms those LSTM networks whose hyper-parameters are determined by grid search, Bayesian optimisation and PSO. Such accurate energy consumption prediction can play an essential role in various areas, including daily building energy management, decision making of facility managers, building information model designs, net-zero energy operation, climate change mitigation and circular economy.