August 2019
Beginner to intermediate
232 pages
3h 50m
Chinese
Content preview from Web容量规划之美
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190
第
6
章
先进的方法
鉴于以最适合的方式使用弹性云开发的重要性，业界和学术界已经提出了一些先
进的技术。例如，
Facebook
采用经典控制理论和比例积分（
PI
）控制器实现快速
反应时间。
Netflix
开发了两种预测算法，其中一种是基于增广线性回归的算法，
另一种是基于快速傅立叶变换（
FFT
）的预测算法。
Netflix
方法的一个关键点是
它的可预测性。具体来说，它的方法学习基于历史数据的请求模式，并随后驱动
放大或缩小操作。这两种方法都已部署到生产环境中。鉴于没有进行横向比较分析，
很难评估这些技术与之前的技术有怎样的冲突。
基于控制理论、排队论、模糊逻辑、神经网络、强化学习、支持向量机、小波变
换、样条回归、模式匹配、卡尔曼滤波、滑动窗口、比例阈值、二阶回归、直方图、
时间
–
系列模型、割线法、投票系统、超前控制等自动扩容的很多方法已经提出来。
在大多数情况下，这些技术“从过去的流量模式和资源使用中学习”，因此无法
适应任何可能由于
Web
流量的动态特性而出现的新模式。
注意
更多信息参见本章后面的
“
延展阅读
”。
在实践中，由于各种各样的原因基于上述方法的自动扩容的适用性是有限的。例如，
基于强化学习的方法需要很长时间才能学习和适应缓慢变化的条件。因此不能将
这种技术应用于通常会遭遇突发流量的实际应用程序扩容过程中。类似地，基于
排队理论的方法对实际复杂系统通常是无效的。此外，这种方法的目的是为了应
对不变的情况，如果应用条件变化，你需要重新计算排队模型。在基于控制理论
的方法中，确定增益参数是意义重大的。
小结
有时候 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.