Scale-up and Scale-out

本文介绍了系统扩展中的两种常见方式:Scale-up(纵向扩展)和Scale-out(横向扩展),通过定义及一个生动的养鱼例子,详细阐述了这两种扩展方式的区别及其应用场景。

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转载自:http://www.cnblogs.com/spork/archive/2009/12/29/1634766.html

来自原小站,曾经迷糊过的东西,表上来,希望对正在迷糊或即将迷糊的人有帮助。

  谈到系统的可伸缩性,Scale-up(纵向扩展)和Scale-out(横向扩展)是两个常见的术语,对于初学者来说,很容易搞迷糊这两个概念,这里总结了一些把概念解释的比较清楚的内容。

  首先来段Wikipedia的,讲的很透彻了。

      Scale vertically (scale up)  

  To scale vertically (or scale up) means to add resources to a single node in a system, typically involving the addition of CPUs or memory to a single computer. Such vertical scaling of existing systems also enables them to leverage Virtualization technology more effectively, as it provides more resources for the hosted set of Operating system and Application modules to share.

  Taking advantage of such resources can also be called “scaling up”, such as expanding the number of Apache daemon processes currently running. 

  Scale horizontally (scale out)

  To scale horizontally (or scale out) means to add more nodes to a system, such as adding a new computer to a distributed software application. An example might be scaling out from one web server system to three.

  As computer prices drop and performance continues to increase, low cost “commodity” systems can be used for high performance computing applications such as seismic analysis and biotechnology workloads that could in the past only be handled by supercomputers. Hundreds of small computers may be configured in a cluster to obtain aggregate computing power which often exceeds that of single traditional RISC processor based scientific computers. This model has further been fueled by the availability of high performance interconnects such as Myrinet and InfiniBand technologies. It has also led to demand for features such as remote maintenance and batch processing management previously not available for “commodity” systems.

  The scale-out model has created an increased demand for shared data storage with very high I/O performance, especially where processing of large amounts of data is required, such as in seismic analysis. This has fueled the development of new storage technologies such as object storage devices.

------------------------------华丽的分割线---------------------------------------

  英语不好?没关系,给你准备了一份中文的,来自这里,他用养鱼来做了个形象的比喻。

  当你只有六七条鱼的时候, <script type="text/javascript"></script><script type="text/javascript"></script><script type="text/javascript"></script><script type="text/javascript"></script> 一个小型鱼缸就够了;可是过一段时间新生了三十多条小鱼,这个小缸显然不够大了。

  如果用Scale-up解决方案,那么你就需要去买一个大缸,把所有沙啊、水草啊、布景啊、加热棒、温度计都从小缸里拿出来,重新布置到大缸。这个工程可不简单哦,不是十分钟八分钟能搞得定的,尤其水草,纠在一起很难分开(不过这 <script type="text/javascript"></script><script type="text/javascript"></script><script type="text/javascript"></script><script type="text/javascript"></script> 跟迁移数据的工程复杂度比起来实在是毛毛雨啦,不值一提)。

  那么现在换个思路,用Scale-out方案,就相当于是你在这个小缸旁边接了一个同样的小缸,两个缸联通。鱼可以自动分散到两个缸,你也就省掉了上面提到的那一系列挪沙、水草、布景等的折腾了。

内容概要:本文档详细介绍了基于事件触发扩展状态观测器(ESO)的分布式非线性车辆队列控制系统的实现。该系统由N+1辆车组成(1个领头车和N个跟随车),每辆车具有非线性动力学模型,考虑了空气阻力、滚动阻力等非线性因素及参数不确定性和外部扰动。通过事件触发ESO估计总扰动,基于动态面控制方法设计分布式控制律,并引入事件触发机制以减少通信和计算负担。系统还包含仿真主循环、结果可视化等功能模块。该实现严格遵循论文所述方法,验证了观测误差有界性、间距误差收敛性等核心结论。 适合人群:具备一定编程基础,对非线性系统控制、事件触发机制、扩展状态观测器等有一定了解的研发人员和研究人员。 使用场景及目标:①研究分布式非线性车辆队列控制系统的理论与实现;②理解事件触发机制如何减少通信和计算负担;③掌握扩展状态观测器在非线性系统中的应用;④学习动态面控制方法的设计与实现。 其他说明:本文档不仅提供了详细的代码实现,还对每个模块进行了深入解析,包括非线性建模优势、ESO核心优势、动态面控制与传统反步法对比、事件触发机制优化等方面。此外,文档还实现了论文中的稳定性分析,通过数值仿真验证了论文的核心结论,确保了系统的稳定性和有效性。建议读者在学习过程中结合代码进行实践,并关注各个模块之间的联系与相互作用。
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