A three-dimensional cloud-scale model has been designed.The governing equations of the model are composed of two groups of equations:one group includes compressible motion equations,continuity equation, pressure equat...A three-dimensional cloud-scale model has been designed.The governing equations of the model are composed of two groups of equations:one group includes compressible motion equations,continuity equation, pressure equation and thermodynamic equation,which are of Eulerian type,and the other consists of cloud- precipitation microphysics equations which are of Lagrangian type.Since the degree of influence of sound wave on the air motion is quite different from that on the temperature or hydrometeors,the time splitting procedure is used in solving governing equations.Both unstaggered and staggered meshes have been utilized.Integra- tion schemes adopted are the Eulerian backward difference method for the unstaggered mesh and semi-implicit method for staggered mesh.Several experiments of modelling have been conducted and a reasonable three- dimensional image of deep convection is obtained.With this model the horizontal and vertical vortex circula- tions are simulated.Furthermore,the effects of horizontal vortex on the formation and development of downdraft within cloud have also been studied.展开更多
Big data is an emerging term in the storage indus- try, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale （or EB-scale） file systems, load bal- ancing in request workloads across a m...Big data is an emerging term in the storage indus- try, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale （or EB-scale） file systems, load bal- ancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improv- ing quality of services. Many good approaches have been pro- posed for load balancing in distributed file systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request dis- tributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication, in this paper, we propose Cloud Cache （C2）, an adaptive and scal- able load balancing scheme for metadata server cluster in EB-scale file systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balanc- ing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. C2 runs as follows： 1） to run adaptive cache diffusion first, if a node is overloaded, load- shedding will be used; otherwise, load-stealing will be used; and 2） to run adaptive replication scheme second, if there is a very popular metadata item （or at least two items） causing a node be overloaded, adaptive replication scheme will be used,in which the very popular item is not split into several nodes using adaptive cache diffusion because of its knapsack prop- erty. By conducting performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C2.展开更多
文摘A three-dimensional cloud-scale model has been designed.The governing equations of the model are composed of two groups of equations:one group includes compressible motion equations,continuity equation, pressure equation and thermodynamic equation,which are of Eulerian type,and the other consists of cloud- precipitation microphysics equations which are of Lagrangian type.Since the degree of influence of sound wave on the air motion is quite different from that on the temperature or hydrometeors,the time splitting procedure is used in solving governing equations.Both unstaggered and staggered meshes have been utilized.Integra- tion schemes adopted are the Eulerian backward difference method for the unstaggered mesh and semi-implicit method for staggered mesh.Several experiments of modelling have been conducted and a reasonable three- dimensional image of deep convection is obtained.With this model the horizontal and vertical vortex circula- tions are simulated.Furthermore,the effects of horizontal vortex on the formation and development of downdraft within cloud have also been studied.
文摘Big data is an emerging term in the storage indus- try, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale （or EB-scale） file systems, load bal- ancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improv- ing quality of services. Many good approaches have been pro- posed for load balancing in distributed file systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request dis- tributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication, in this paper, we propose Cloud Cache （C2）, an adaptive and scal- able load balancing scheme for metadata server cluster in EB-scale file systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balanc- ing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. C2 runs as follows： 1） to run adaptive cache diffusion first, if a node is overloaded, load- shedding will be used; otherwise, load-stealing will be used; and 2） to run adaptive replication scheme second, if there is a very popular metadata item （or at least two items） causing a node be overloaded, adaptive replication scheme will be used,in which the very popular item is not split into several nodes using adaptive cache diffusion because of its knapsack prop- erty. By conducting performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C2.
文摘随着算力网络中计算资源与虚拟化设备的广泛应用,在算力网络虚拟化中,针对云集群弹性伸缩策略基于阈值的响应式触发过程中存在的弹性滞后问题,提出一种基于Transformer的预测式云集群资源弹性伸缩方法(Predictive Cloud Cluster Resource Elastic Scaling Method Based on Transformer,Cloudformer).该方法利用序列分解模块将云集群数据分解为趋势项和季节项,趋势项采用双系数网络分别对输入空间预测的均值和方差进行归一化和反归一化,季节项采用融合傅里叶变换的频域自注意力模型进行预测,并在模型训练过程中使用指数移动平均模型动态调整训练损失的误差范围.实验结果表明,对比最先进的五种预测式弹性伸缩算法,本文所提出的方法在保持较低的模型训练和推理时间下,不同预测窗口单变量与多变量预测均方误差分别降低了10.07%和10.01%.
