August 2019
Beginner to intermediate
232 pages
3h 50m
Chinese
Content preview from Web容量规划之美
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183
自动扩缩容
•
为
RPS
生产历史时间
–
系列，确定每一次启动
A
start
时间的
RPS
的变化，其中
A
start
表示应用程序启动时间。这将产生一个具有这些数据点的时间序列：
RPS
Astart
－
RPS
0
RPS
Astart+1
－
RPS
1
RPS
Astart+2
－
RPS
2
RPS
Astart+3
－
RPS
3
·
·
·
RPS
t
表示在时间
t
的
RPS
。派生的时间
–
系列，简称
RPS
变化滚动，捕捉到
A
start
启动时间的任何窗口宽度
RPS
的变化。
•
用
R_RPS
计算滚动时间－系列的第九十九百分位。
•
计算
γ
= T
U
－
R
RPS
。参数
γ
是放大的有效阈值。使用第九十九百分位数的滚
动
RPS
改变时间序列与先前提出的积极的上行指南一致。
图
6-7
的上部分展示一个应用程序的
RPS
时间—系列（分钟粒度）。应用程序的
启动时间为
30
分钟。相应的滚动
RPS
时间—系列如图
6-7
的下部分所示。滚动时
间序列的第九十九个百分位数是
1.109
。
算法
3
详细说明了确定扩缩容阈值的参数和步骤（用于扩容和缩容）。缩容值
D
和扩容值
U
是算法的输入。用于定义
T
U
和
T
D
的常数
0.90
和
0.50
是根据经验确定的。
以减少对用户体验的影响和
ASG
的不充分利用。随着传入流量的增加，算法
3
的
循环
L1
会相应地扩容
ASG
；随着传入流量的减少，算法
3
的循环
L2
会相应地缩
容
ASG
。不像扩容，缩容参数
D
的阈值不需要调整，因为亚马逊
EC2
上的应用程
序不会在实例终止时引起长时间的延迟。
184
第
6
章
Figure 6-7. Rolling change in RPS
n
for ...
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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.