Vladislav Shpilevoy PRO
Database C developer at Tarantool. Backend C++ developer at VirtualMinds.
Ubisoft в Google Cloud: автомасштабирование игрового кластера
Владислав Шпилевой
Watch interactively
Plan
Cluster scaling
Scaling in Ubisoft
Google Cloud
New scaling algorithm
Testing
Future plans
Cluster scaling [1]
Add or drop instances depending on load
Two types
Bare metal
Cloud
Cluster scaling [2]
Metal
Cloud
Buy or rent, deliver, install
- Hours, days, weeks long
- Excess reservation
- Can be cheaper
POST create_instances HTTP/1.1
Send an HTTP request
- Seconds, minutes
- Accurate
- Can be more expensive
Cluster scaling [3]
Cluster load
Capacity with good scaling
Capacity with bad scaling
Money waste
Scaling in Ubisoft
Bare metal cluster
- Expensive powerful servers
- Plan for load spikes
Now moving to cloud for better scaling and more
Load
Very volatile
600 000 players
200 000 players
Region Europe West
Region
USA East
Region Asia
Instance groups
Google Autoscaler
For Managed Instance Groups
Google Autoscaler
Ungraceful shutdown looses the state
Servers
Load
For Managed Instance Groups
Automatic cluster resize based on metrics
Players are disconnected
Saves are lost
New Autoscaler
From Ubisoft
New Autoscaler
Instance Group
Google Cloud Region
Autoscaler
Players
Greeting
State
...
Commands
Google Cloud REST API
/create_instances
/delete_instances
Scale up
Based on threshold
Capacity
Threshold
Players
Example
Average player count
Servers
Scale down
Based on threshold
Capacity
Threshold
Players
Safety Timeout
Two-phase shutdown
ACTIVE
Autoscaler sends decommission request
DECOMMISSIONING
Server enters DECOMMISIONING state
No new players
Players eventually leave
Save games are uploaded
Server enters DECOMMISIONED state
DECOMMISSIONED
Autoscaler kills the machine
Extensions
Restoration
Ext: Restoration [1]
Scale down
Scale up
Load is below scale down threshold
Decommission a couple of servers
But now load grows
Add new servers
Some servers waste money. Have space, but can't be used
Need to reuse the machines
Ext: Restoration [2]
Restore the old servers
Then add new. All the space is used when need to scale up
Extensions
Quorum
Ext: Quorum [1]
Need to scale up
Add servers
But they are broken
Add more
Still broken
Some machines don't work but waste money. Can continue infinitely
Protect from infinite scale up
Ext: Quorum [2]
Cluster size
Connected count
Quorum – minimal % of connected nodes to approve scaling
cluster_sizeconnected>=quorum
\frac{connected}{cluster\_size} >= quorum
cluster_sizeconnected<quorum
\frac{connected}{cluster\_size} < quorum
Extensions
Adaptive thresholds
Ext: Adaptive thresholds [1]
Server capacity: 100
Scale up threshold: 90
~10% reservation
Example:
A single constant threshold does not scale
10%∗10 000=100010%∗100=10
10\% * 10\ 000 = 1000 \\
10\% * 100 = 10
Reservation cost is x100 bigger
Assume 2 servers are filled per second,
Server boot time is 3 minutes. Then:
- 10 servers give 5 seconds – not enough
- 1000 servers give 8 minutes – too much
100 servers
10 000 servers
10 reserve
1000 reserve
Ext: Adaptive thresholds [2]
10%
5%
2%
"thresholds": [
{
"server_count": 100,
"scale_up_player_count": 90,
"scale_down_player_count": 70
},
{
"server_count": 1000,
"scale_up_player_count": 95,
"scale_down_player_count": 80
},
{
"server_count": 5000,
"scale_up_player_count": 98,
"scale_down_player_count": 90
},
]
Reservation is changed depending on server count
Capacity
Threshold
Players
Extensions
Prediction
Ext: Prediction [1]
Boot time is never zero
In reality the booting takes time
Booting
How scaling looks in theory
Example:
- 200 servers
- 1 server filled per second
- 10% reservation
- Boot time is 3 mins
The reserve ends in 20 secs. People wait for 2 min 40 secs
The cluster can and will be overloaded
Ext: Prediction [2]
Existing solutions
Electricity load (at winter)
Game cluster load (one of)
- Complex solutions, up to neural networks
- Days/weeks/months of training
Time of day
00
06
12
14
18
20
00
Prediction is used in
- Electricity
- Water
- Web services
- Games
Solutions are very similar
Ext: Prediction [3]
Linear regression
Quadratic regression
y=a+b∗x
y = a + b*x
y=a+b∗x+c∗x2
y = a + b*x + c*x^2
Prediction
Train
Train
Prediction
"prediction": {
"algorithm": "quadratic_regression",
"train_interval_sec": 600,
"sample_interval_sec": 10
}
~120 C++ code lines
~240 C++ code lines
Ext: Prediction [4]
Adapts for O(1) time
(X,Y)={(x0,y0),...,(xn,yn)}
(X, Y) = \{(x_0, y_0), ..., (x_n, y_n)\}
All the same for the quadratic regression
y=a+b∗x;
y = a + b*x;
Find for each new point
Sxx=∑n=1Nxi2−N∗mean(X)2;
S_{xx} = \sum_{n=1}^{N}x_i^2 - N*mean(X)^2;
a=mean(X)−b∗mean(Y);
a = mean(X) - b * mean(Y);
b=SxxSxy;
b = \dfrac{S_{xy}}{S_{xx}};
Sxy=∑n=1Nxi∗yi−N∗mean(X)∗mean(Y);
S_{xy} = \sum_{n=1}^{N}x_i*y_i- N*mean(X)*mean(Y);
mean(Y)=N∑n=1Nyi;
mean(Y) = \dfrac{\sum_{n=1}^{N}y_i}{N};
mean(X)=N∑n=1Nxi;
mean(X) = \dfrac{\sum_{n=1}^{N}x_i}{N};
Store and update on each step
Calculate on each step
Ext: Prediction [5]
No prediction
Linear regression
Quadratic regression
100 000 players
160 000 players
20 minutes
Queue
Capacity
Extensions
Rolling update
Ext: Rolling update [1]
Full cluster upgrade
There is a cluster
Group template:
The template is changed. Has a new version of the game
Google's autoscaler recreates old instances in packs. This is rolling update
Google automatically assigns it to new instances
Ext: Rolling update [2]
New instances get the new template automatically. Only need to find and recreate the old ones
Google Cloud REST API
/get_info
{
"template": <templ>
}
{
"name": <name>,
"template": <templ>,
}
Autoscaler downloads the latest template from Google
The servers send their template to Autoscaler
Autoscaler finds outdated instances by their template
Ext: Rolling update [3]
Part 1: during scale down prefer to decommission outdated servers
Scale down
Scale up
New template
Old template
Decommission
Need to scale down
Decommission outdated servers first
Need to scale down again
Now can decommission new servers
Ext: Rolling update [4]
Part 2: during normal work upgrade in batches
Scale down
Scale up
New template
Old template
Decommission
No scaling is required. But has outdated servers
Introduce upgrade quota – max % of outdated machines to upgrade at a time
upgrade_quota = 20%
Outdated are decommissioned by the quota. But it brings disbalance
Add new machines. Hence the quota is max over-reservation
Eventually the old ones are deleted
The process continues. The update is rolling
Ext: Rolling update [5]
Part 3: during scale up try not to restore outdated servers
Scale down
Scale up
New template
Old template
Decommission
Scale down is in progress
upgrade_quota = 20%
But now load grows – need to scale up!
Restore some outdated servers. But keep upgrading
Create new servers to finish scale up
Restored
Extensions
Canary deployment
Ext: Canary deployment [1]
Partial cluster upgrade
There is a cluster
Group template:
There is a new version of the game. Want to try it on a few nodes – it is canary deployment
In Google's group settings can specify 2 templates, one with canary size
50%
Google automatically assigns templates to new instances
Google's built-in autoscaler can upgrade existing instances to meet the target size
Ext: Canary deployment [2]
The same as rolling update
Download canary settings from Google's API
Let Google assign templates automatically
Prefer to decommission excess canary instances, use upgrade quota
"versions": [
{
"instanceTemplate": <templ_old>,
},
{
"instanceTemplate": <templ_new>,
"targetSize": {
"percent": <size>,
}
}
]
+
-
Ext: Canary deployment [3]
Scale down
Scale up
Canary template
Default template
Decommission
Balanced cluster
Group template:
upgrade_quota = 20%
Now a canary template is installed
50%
Start an upgrade using the quota. Target size is 5 instances
Continue the upgrade
Eventually, there are 50% canaries, rounded up to 5 instances
Google assigns templates automatically. 6 of 12 are canaries – 50%.
Now need 3 more servers!
Now need to decommission 6 servers!
The autoscaler decommissions 3 canaries and 3 defaults
The balance is respected – 3 of 6 are canaries
Extensions
Zone balance
Ext: Zone balance [1]
Instances can live in multiple zones. They must be balanced to protect from outage
Cluster has multiple zones
When add new instances, Google spreads them evenly automatically
Google built-in autoscaler keeps the zones balanced when scales down
If one zone is out, it affects only cluster_size/zone_count part of the cluster
Ext: Zone balance [2]
Download zone list
Let Google assign zones automatically
Prefer to decommission servers from bloated zones
"zones": [
{
"zone": <zone1>,
},
{
"zone": <zone2>,
},
{
"zone": <zone3>,
}
],
-
+
The same as rolling update
Ext: Zone balance [3]
Need to scale up
Scale down
Scale up
Create new instances. They are assigned to zones by Google automatically
Need to scale down
Decommission servers evenly across the zones
Now need to scale up again!
Restore servers evenly across the zones
Decisions
Deal with all extensions
Server selection [1]
Is outdated
Is canary,
canary size > target
Isn't canary,
canary size <= target
Zone balance
Is empty
100000
10000
1000
100 + zone size %
1
Decommission of 1 server
Calculate server scores:
Decommission the server with the biggest score
Digit positions reveal score value components
Server selection [2]
– outdated
– new
– canary
Canary target – 25%
Need to decommission 1 server
101125
101125
1125
1125
1125
125
1150
1150
1150
1150
150
150
Calculate scores
The outdated servers have the biggest score: 100000 + 1000 + 125
Kill one of them
The scores are updated due to new zone size percentage
101118
1118
1128
1128
1128
128
1155
1155
1155
1155
155
155
Kill the next outdated server
The scores are updated again
1110
1130
1130
130
1160
1160
1160
1160
160
160
Kill 2 more servers similarly
The scores now are more interesting – there are too many canaries. 25% is 2 now
113
138
138
10138
150
150
10150
10150
The next kill prefers a canary from the biggest zone
The scores are updated again and so on ...
1115
1143
1143
143
1143
1143
143
Server selection [3]
Restoration of 1 server
Calculate server scores:
Restore the server with the biggest score
Is not outdated
Is canary,
canary size < target
Isn't canary,
canary size >= target
Zone balance
100000
10000
1000
200 - zone size %
Digit positions reveal score value components
Testing [1]
Autoscaler – 5800 lines C++
Basic algorithms
Autoscaler library
Executable
Thresholds, prediction, decisions.
Unit tests
Talk to Google API and the servers.
Tests with Google Cloud Emulator
Does common work not related to scaling. Manual tests
Testing [2]
Autoscaler in Python
Copy of the autoscaler to play with algorithms in real-world datasets
Loadtests
Full game backend in Cloud + thousands of bots
Playtests
~200 real people play in Cloud
Comparison
Google autoscaler
Ubisoft autoscaler
Quorum:
No, but ungraceful
Restoration:
Yes, but graceful
Rolling update:
Canary deployment:
Zone balance:
Prediction:
Graceful:
3 days train, but smart
3 mins train, but 'special'
Future plans
Go live
Bare metal + cloud combined
Cloud machines
Metal machines
Conclusion
Perfect autoscaler does not exist
Load prediction is necessary and can be simple
In cloud save money on scaling
More presentations
Jobs
More presentations
Jobs
Linear regression [1]
y=a+b∗x,(x,y)=(x1,y1),...,(xn,yn)
y = a + b*x, (x, y) = {(x_1, y_1), ..., (x_n, y_n)}
Need to find A and B. Using "least squares" method solve the equation system approximately:
y1=A+B∗x1...yn=A+B∗xn
y_1 = A + B*x_1 \\
... \\
y_n = A + B*x_n
Solution according to "least squares":
B=Sxy/SxxA=meany−B∗meanxsumvar=∑n=1Nf(vari)meanvar=sumvar/NSxx=sumxx−n∗meanx∗meanxSxy=sumxy−n∗meanx∗meany
B = S_{xy} / S_{xx} \\
A = mean_y - B * mean_x \\
\\
sum_{var} = \sum_{n=1}^{N}f(var_i) \\
mean_{var} = sum_{var} / N \\
\\
S_{xx} = sum_{xx} - n * mean_x * mean_x \\
S_{xy} = sum_{xy} - n * mean_x * mean_y
Linear regression [2]
sumx,sumy,sumxx,sumxy
sum_x, sum_y, sum_{xx}, sum_{xy}
These values need to be cached in the model:
By them can always get A and B in a few operations by the previous formulas.
This is how to update them when a new point is added and the oldest is dropped
sumx=old_sumx+xn+1−x0sumy=old_sumy+yn+1−y0sumxx=old_sumxx+xn+12−x02sumxy=old_sumyy+yn+12−y02
sum_x = old\_sum_x + x_{n+1} - x_0 \\
sum_y = old\_sum_y + y_{n+1} - y_0 \\
sum_{xx} = old\_sum_{xx} + x_{n+1}^2 - x_0^2 \\
sum_{xy} = old\_sum_{yy} + y_{n+1}^2 - y_0^2
The latest A and B can be cached to make predictions
Quadratic regression [1]
y=a+b∗x+c∗x2,(x,y)=(x1,y1),...,(xn,yn)
y = a + b*x + c*x^2, (x, y) = {(x_1, y_1), ..., (x_n, y_n)}
Need to find A, B, and C. Using "least squares" method solve the equation system approximately:
y1=A+B∗x1+C∗x12...yn=A+B∗xn+C∗xn2
y_1 = A + B*x_1 + C*x_1^2 \\
... \\
y_n = A + B*x_n + C*x_n^2
Quadratic regression [2]
Solution according to "least squares":
B=Sxx∗Sxxxx−Sxxx2Sxy∗Sxxxx−Sxxy∗Sxxx C=Sxx∗Sxxxx−Sxxx2Sxxy∗Sxx−Sxy∗Sxxx A=meany−B∗meanx−C∗meanxx
B = \dfrac{S_{xy} * S_{xxxx} - S_{xxy} * S_{xxx}}{S_{xx} * S_{xxxx} - S_{xxx}^2} \\~\\
C = \dfrac{S_{xxy} * S_{xx} - S_{xy} * S_{xxx}}{S_{xx} * S_{xxxx} - S_{xxx}^2} \\~\\
A = mean_y - B * mean_x - C * mean_xx
sumvar=∑n=1Nf(vari)meanvar=sumvar/NSxx=sumxx−n∗meanx∗meanxSxy=sumxy−n∗meanx∗meanySxxx=sumxxx−n∗meanx∗meanxxSxxy=sumxxy−n∗meanxx∗meanySxxxx=sumxxxx−n∗meanxx2
sum_{var} = \sum_{n=1}^{N}f(var_i) \\
mean_{var} = sum_{var} / N \\
\\
S_{xx} = sum_{xx} - n * mean_x * mean_x \\
S_{xy} = sum_{xy} - n * mean_x * mean_y \\
S_{xxx} = sum_{xxx} - n * mean_x * mean_{xx} \\
S_{xxy} = sum_{xxy} - n * mean_{xx} * mean_y \\
S_{xxxx} = sum_{xxxx} - n * mean_{xx}^2
Quadratic regression [3]
sumx,sumy,sumxx,sumxy,sumxxy,sumxxx,sumxxxx
sum_x, sum_y, sum_{xx}, sum_{xy}, sum_{xxy}, sum_{xxx}, sum_{xxxx}
These values need to be cached in the model:
By them can always get A, B, and C in a few operations by the previous formulas.
This is how to update them when a new point is added and the oldest is dropped
sumx=old_sumx+xn+1−x0sumy=old_sumy+yn+1−y0sumxx=old_sumxx+xn+12−x02sumxy=old_sumyy+yn+12−y02sumxxy=old_sumxxy+xn+12∗yn+1−x02∗y0sumxxx=old_sumxxx+xn+13−x03sumxxxx=old_sumxxxx+xn+14−x04
sum_x = old\_sum_x + x_{n+1} - x_0 \\
sum_y = old\_sum_y + y_{n+1} - y_0 \\
sum_{xx} = old\_sum_{xx} + x_{n+1}^2 - x_0^2 \\
sum_{xy} = old\_sum_{yy} + y_{n+1}^2 - y_0^2 \\
sum_{xxy} = old\_sum_{xxy} + x_{n+1}^2 * y_{n+1} - x_0^2 * y_0 \\
sum_{xxx} = old\_sum_{xxx} + x_{n+1}^3 - x_0^3 \\
sum_{xxxx} = old\_sum_{xxxx} + x_{n+1}^4 - x_0^4
The latest A, B, and C can be cached to make predictions
Quadratic regression [4]
The formulas are heavy. The temporary calculations must be reused as much as possible during update. For example, to get this:
xi2,xi3,xi4
x_i^2, x_i^3, x_i^4
Do the following:
x2=x∗xx3=x2∗xx4=x3∗x
x2 = x * x \\
x3 = x2 * x \\
x4 = x3 * x
Instead of:
x2=x∗xx3=x∗x∗xx4=x∗x∗x∗x
x2 = x * x \\
x3 = x * x * x \\
x4 = x * x * x * x
Quadratic regression [5]
The formulas involve 6th power of X – it won't fit into 64 bit integers and will loose all precision in doubles. Use a decimal number library to make the calculations precise. For instance decNumber works in C and C++.
Меня зовут Владислав, я работаю в Ubisoft - это крупнейшая европейская компания-разработчик игр. Моя студия находится в Германии, в городе Дюссельдорф. Работаю я там уже пару лет, большую часть которых занимался разработкой вещей связанных с облаком, в том числе я сделал сервис автомасштабирования для Google Cloud, про который сегодня рассказываю. До этого и параллельно с этим я работаю в базе данных Tarantool.
Главная моя цель сегодня, буду честен - рассказ ради рассказа. Я люблю выступать на конференциях и рассказывать, как я сделал что-то крутое, и почему оно крутое. Но еще лучше, если суть проблем и принципы решений, которые я сегодня покажу, кто-то сможет понять и даже использовать сам. Доклад не привязан к теме игр исключительно. То, что буду рассказывать, применить можно много где.
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Ubisoft в Google Cloud: автомасштабирование игрового кластера Владислав Шпилевой
Ubisoft в Google Cloud: автомасштабирование игрового кластера
By Vladislav Shpilevoy
Ubisoft в Google Cloud: автомасштабирование игрового кластера
- 1,124
