其次分析了负载的特性，为负载预测提供了依据。
Secondly, the load characteristics are analyzed, which are the basis of load prediction.
地方选区的指导负载预测并没有多大的中心疲软，这意味着它会为买家的平均罚款。
The GC's steering loads up predictably and doesn't have much on-center slack, which means it will work fine for the average buyer.
本文介绍了协同学理论，并提出了多路径负载预测基于最大熵原理的自动协调的方法。
This paper introduces synergetic theory and proposes multi-path load forecasting automatically coordinated approach based on the maximum entropy principle.
实验结果表明，与MPI直接分配方式相比，基于BP算法负载预测设计的调度系统的性能有了一定的提高。
Result shows that the load scheduling system based on the BP algorithm prediction improves performance certainly compared with the way of MPI direct allocation.
提出一种基于遗传神经网络的主机负载预测模型，并基于该模型设计了集中式任务调度算法CJD - HLP。
A host load prediction model based on heredity-neural network is proposed, and center task scheduling algorithm CJD-HLP based on the model is designed.
这使您很难预测单一实例的实际延续时间，尤其是在负载很高的系统上。
This makes it hard to predict the actual duration of single instances, especially on a heavily loaded system.
这些应用程序必须处理不可预测的负载，它们需要始终可用，而且处于不断更改之中。
These applications have to handle an unpredictable load, they have to be available all the time, and they are constantly changing.
利用模拟的流程，可以预测如何在各种不同的场景中执行业务；例如，在不同的季节，会有不同的使用者负载。
With processes being simulated, the business can predict how it will perform in various different scenarios; for example, different consumer loads during different seasons.
当您了解某个负载之后，就更容易预测向第二个服务器添加另一个负载时会发生什么。
Once you know what each load is separately, it's easier to predict what will happen if you add a load to the second server.
当您了解某个负载之后，就更容易预测向第二个服务器添加另一个负载时会发生什么。
Once you know what each load is separately, then it's easy to predict what will happen when you add a user load to the second server.
惟一的例外是，具有较少写活动和不可预测的查询工作负载的应用程序，这种应用程序难于定义更明确的索引。
The only exception could be applications with low write activity and unpredictable query workload such that more specific indexes are hard to define.
这种类型的行为可以确保我们了解基准工作负载的上限和下限，并将预测的值限制在这些已知的范围之内。
This type of behavior caused us to make sure we knew the upper and lower boundaries of a benchmark workload and limited the estimated values to those that were between the known values.
理解这些工作负载模式让我们可以计划和预测DPAR的工作负载增长(在本例中，在9月份的劳动节附近)。
Understanding these workload patterns allowed us to plan and anticipate the workload increases to the DPARs (in this example, around Labor Day in September).
AIX 6.1TL4SP2中提供了此命令，此命令也可在POWER4以上的系统中运行，以便预测将在具有AME功能的POWER7系统中使用的工作负载。
This command is available from AIX 6.1 TL4 SP2 and can be run on the older system starting from POWER4 to estimate the workload for use on the POWER 7 system with AME.
实验表明：该方法能够有效预测非平稳的服务器负载序列，预测精度明显高于传统预测方法。
Experiments results show that this method can predict non-stationary server load series efficiently and has higher prediction accuracy than traditional methods.
然后分别对各信号进行一步预测并组合预测结果，获得原始负载的最终预测。
After one-step-ahead prediction, the predicted results of these signals are combined into the final predicted result of the original load series.
计算机程序可以预测锻模的装料、负载、能量和缺陷的形成。
Computer programs can predict die fill, load, energy, and defect formation.
传统的图形绘制集群采用前向着色，存在光照着色功能有限、计算负载难以预测等缺陷。
Traditional forward shading based sort-first rendering clusters have the difficulty of load prediction and limited light shading performance.
并以某地历年三月的负荷为例，预测并验证了负载的精度。
Taking load of March in every years in an area as an example, and the accuracy of load was predicted and verified.
有限元方法rsd为：观测有限元方法是相当准确的预测能力变形行为，如辊接触应力，轧制负载和轧制力矩为区署要求。
FE method in RSD: observations FE methods are capable of predicting quite accurate deformation behaviour such as roll contact stresses, rolling load, and rolling torque required for RSD.
设定负载测试混合模型选项，可以让您对进行负载测试中的网站或应用程序，更为准确地预测其预期真实使用情况。
You configure load test mix modeling options to more accurately predict the expected real-world usage of a Web site or application that you are load-testing.
分布式系统中为了获得高效的动态负载均衡，需要对主机负载进行有效的预测，这区别于网络流量的预测。
In order to gain effectively dynamic load balancing, it is necessary to predict host load accurately, and this is different from network flow prediction.
针对交流电机伺服系统中负载转矩不可预测性的特点，提出一种基于状态观测器的负载转矩自适应辨识方法。
Aiming at characteristics of unpredictability the load torque used in ac motor servo system, a method of load torque adaptive identification based on state observer is presented.
针对当前已有预测算法不实时、对负载变化不敏感的问题，结合网格中任务的特点，提出新的基于分块的预测算法。
Concerning the issues of no real-time and not sensitive to load change, this paper proposed a new prediction algorithm based on chunk taking account of the task's characteristics in grid.
仿真实验表明，在不同负载情况下，系统能够动态地、较精确地预测出流媒体传输所需的带宽变化范围，实现了网络流间的公平传输，并满足服务质量控制的要求。
The range can be predicted accurately under different network load. The fair allocation between multimedia and TCP flows and the requirements of quality of services are realized.
为了使负载向量值尽可能的预测下一分配时刻的负载，本系统采用平滑后的负载信息构建负载向量。
In order that the value of load vector can forecast the load of the next time for tasks assignment, this system utilizes the disposed load information to construct load vector.
在此基础之上，通过实验验证了在负载平衡框架DLBFG支持下，基于网格的交通流预测的性能得到了明显的提升。
On this basis, through experiments in DLBFG support, the performance of grid-based traffic flow forecasting has been remarkably improved.
本文主要研究并行计算系统中的负载平衡算法与并行执行时间预测问题。
This paper focus on the research of load balancing algorithm and parallel execution time prediction for parallel computing systems.
实验结果表明，静态模型的缓存命中率预测结果比实际高70%以上，而该模型则能适应负载的变动，预测结果与实际结果差别在10%左右。
The performance of the static model differs from the actual results by 70% while the performance of the dynamic model differs by only 10%.
结合主机负载和任务执行时间的线性关系，研究分析了一种基于主机负载的任务执行时间预测算法。
In this dissertation, the authors studied and analyzed an algorithm predicting the execution time of task by the linear relationship between host load and the execution time of task.
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