CDP:Scheduled Scale Out pattern

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[[Category: CDP: All Patterns]] [[Category: CDP: Patterns for Processing Dynamic Content]] {{List of CDP}} {{NoT}}  
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[[Category: CDP: All Patterns]] [[Category: CDP: Patterns for Processing Dynamic Content]] {{List of CDP}} {{Architect|file=Oko_chang.png
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|link=https://twitter.com/#!/oko_chang
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|name=@oko_chang}}  
 
Increasing or Decreasing the Number of Servers Following a Schedule  
 
Increasing or Decreasing the Number of Servers Following a Schedule  
  
 
== Problems to Be Solved ==  
 
== Problems to Be Solved ==  
The Scale-Out Pattern is effective when handling high levels of traffic in a web service that is structured in a cloud environment. However, when monitoring the load status to manually add virtual servers, or automatically adding instances depending on the load statuses of the virtual servers, the launching of instances may not be able to keep up when there is a sudden increase in accesses (cases wherein traffic doubles in less than five minutes).  
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The Scale-Out Pattern is effective when handling high levels of traffic in a web service that is structured in a Cloud environment. However, when monitoring the load status to manually add virtual servers, or when automatically adding instances depending on the load statuses of the virtual servers, the creation of instances may not be able to keep up with the demand when there is a sudden increase in accesses (such as a case where traffic doubles in less than five minutes).  
  
 
== Explanation of the Cloud Solution/Pattern ==  
 
== Explanation of the Cloud Solution/Pattern ==  
When the timing with which there will be an instantaneous increase in accesses is understood, then scale-out through scheduling is effective. While the fundamental structure is similar to that of the Scale-Out Pattern, the key distinctive feature is that of performing the scale-out through specifying the timing with which to do so. Completing the scale-out in advance makes it possible to handle a rapid increase in traffic with a robust system, and performing the scaling immediately prior to the rapid increase in traffic can minimize wasted costs.  
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Use scale-out through scheduling when the timing when there will be an sudden increase in traffic is understoodi n advance. While the fundamental structure is similar to that of the Scale-Out Pattern, the key difference is that of performing the scale-out through specifying the timing for doing so. You can handle a rapid increase in traffic robustly by completing the scale-out in advance, and you can minimize wasted costs by performing the scaling immediately prior to the rapid increase in traffic.  
  
 
=  Implementation =  
 
=  Implementation =  
Auto Scaling in AWS has a function for specifying the time at which settings are to be changed. This function can be used to configure scheduled scale-out. Scale-in is also possible through specifying a time band wherein the traffic is anticipated to settle down.  
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Auto Scaling in AWS has a function for specifying a time for changing the settings. You can use this function to configure scheduled scale-out. You can also perform Scale-in through specifying a time band when the traffic is anticipated to settle down.  
  
 
* Reference the Scale-Out Pattern to set up Auto Scaling (including scale-out triggers and scale-in triggers).
 
* Reference the Scale-Out Pattern to set up Auto Scaling (including scale-out triggers and scale-in triggers).
* Specify the timing with which to increase the number of EC2 instances and change, to the number of instances to be provided, the setting for "minimum number of instances (– – – min-size)." * At the specified time, new EC2 instances will be launched up to the minimum number of instances specified.
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* Specify the timing with for increasing the number of EC2 instances and change the setting for "minimum number of instances (– – – min-size)" to the number of instances to be provided. * At the specified time, new EC2 instances will be launched, up to the minimum number of instances specified.
 
* If the minimum number of instances again is reduced with the timing with which the load is to settle down, then scale-in will follow the trigger that has been set.  
 
* If the minimum number of instances again is reduced with the timing with which the load is to settle down, then scale-in will follow the trigger that has been set.  
  
 
== Configuration ==     
 
== Configuration ==     
https://cacoo.com/diagrams/6wNg0ISJczU5Pz1m-4,813A.png
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https://cacoo.com/diagrams/2XNdewVsgellO3x8-4813A.png
  
 
== Benefits ==  
 
== Benefits ==  
* This makes it possible to increase the number of EC2 instances following the schedule with which the traffic volume is anticipated to increase.
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* This allows you to increase the number of EC2 instances following the schedule with which the traffic volume is anticipated to increase.
* This reduces costs because the number of EC2 instances is reduced when there is little traffic.
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* This reduces your cost because the number of EC2 instances is reduced when there is little traffic.
 
* When compared to scale-up, the limit on processing capability is extremely high because the required number of EC2 instances can be provided in parallel, under the control of the ELB.  
 
* When compared to scale-up, the limit on processing capability is extremely high because the required number of EC2 instances can be provided in parallel, under the control of the ELB.  
  
 
== Cautions ==
 
== Cautions ==
* The specified time is in terms of UTC.
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* Be aware that the specified time is in terms of UTC.
* When there is to be a sudden increase in traffic, the ELB must be scaled-out in addition to the EC2 instances. In this case, pre-warming is requested.  
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* When there is to be a sudden increase in traffic, you must scale-out the ELB in addition to scaling-out the EC2 instances. In this case, you should perform pre-warming.  
  
 
== Other ==   
 
== Other ==   
See the Scale-out Pattern.
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See the [[CDP: Scale Out Pattern |Scale Out Pattern]].

Latest revision as of 07:35, 26 November 2012

Architect

Increasing or Decreasing the Number of Servers Following a Schedule

Contents

Problems to Be Solved

The Scale-Out Pattern is effective when handling high levels of traffic in a web service that is structured in a Cloud environment. However, when monitoring the load status to manually add virtual servers, or when automatically adding instances depending on the load statuses of the virtual servers, the creation of instances may not be able to keep up with the demand when there is a sudden increase in accesses (such as a case where traffic doubles in less than five minutes).

Explanation of the Cloud Solution/Pattern

Use scale-out through scheduling when the timing when there will be an sudden increase in traffic is understoodi n advance. While the fundamental structure is similar to that of the Scale-Out Pattern, the key difference is that of performing the scale-out through specifying the timing for doing so. You can handle a rapid increase in traffic robustly by completing the scale-out in advance, and you can minimize wasted costs by performing the scaling immediately prior to the rapid increase in traffic.

Implementation

Auto Scaling in AWS has a function for specifying a time for changing the settings. You can use this function to configure scheduled scale-out. You can also perform Scale-in through specifying a time band when the traffic is anticipated to settle down.

  • Reference the Scale-Out Pattern to set up Auto Scaling (including scale-out triggers and scale-in triggers).
  • Specify the timing with for increasing the number of EC2 instances and change the setting for "minimum number of instances (– – – min-size)" to the number of instances to be provided. * At the specified time, new EC2 instances will be launched, up to the minimum number of instances specified.
  • If the minimum number of instances again is reduced with the timing with which the load is to settle down, then scale-in will follow the trigger that has been set.

Configuration

2XNdewVsgellO3x8-4813A.png

Benefits

  • This allows you to increase the number of EC2 instances following the schedule with which the traffic volume is anticipated to increase.
  • This reduces your cost because the number of EC2 instances is reduced when there is little traffic.
  • When compared to scale-up, the limit on processing capability is extremely high because the required number of EC2 instances can be provided in parallel, under the control of the ELB.

Cautions

  • Be aware that the specified time is in terms of UTC.
  • When there is to be a sudden increase in traffic, you must scale-out the ELB in addition to scaling-out the EC2 instances. In this case, you should perform pre-warming.

Other

See the Scale Out Pattern.

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