Amazon EC2 Auto Scaling is a fully managed service designed to launch or terminate Amazon EC2 instances automatically to help ensure you have the correct number of Amazon EC2 instances available to handle the load for your application. Amazon EC2 Auto Scaling helps you maintain application availability through fleet management for EC2 instances, which detects and replaces unhealthy instances, and by scaling your Amazon EC2 capacity up or down automatically according to conditions you define.
You can use Amazon EC2 Auto Scaling to automatically increase the number of Amazon EC2 instances during demand spikes to maintain performance and decrease capacity during lulls to reduce costs. Using AWS Auto Scaling to configure scaling policies for all of the scalable resources in your application is faster than managing scaling policies for each resource via its individual service console.
This ensures that your application has the compute capacity that you expect. You can use Amazon EC2 Auto Scaling to automatically scale your Amazon EC2 fleet by following the demand curve for your applications, reducing the need to manually provision Amazon EC2 capacity in advance.
For example, you can set a condition to add new Amazon EC2 instances in increments to the ASG when the average utilization of your Amazon EC2 fleet is high; and similarly, you can set a condition to remove instances in increments when CPU utilization is low.
If you have predictable load changes, you can set a schedule through Amazon EC2 Auto Scaling to plan your scaling activities. Fleet management refers to the functionality that automatically replaces unhealthy instances and maintains your fleet at the desired capacity. Amazon EC2 Auto Scaling fleet management ensures that your application is able to receive traffic and that the instances themselves are working properly.
When Auto Scaling detects a failed health checkit can replace the instance automatically. The dynamic scaling capabilities of Amazon EC2 Auto Scaling refers to the functionality that automatically increases or decreases capacity based on load or other metrics. Target tracking is a new type of scaling policy that you can use to set up dynamic scaling for your application in just a few simple steps. With target tracking, you select a load metric for your application, such as CPU utilization or request count, set the target value, and Amazon EC2 Auto Scaling adjusts the number of EC2 instances in your ASG as needed to maintain that target.
It acts like a home thermostat, automatically adjusting the system to keep the environment at your desired temperature. An Amazon EC2 Auto Scaling group ASG contains a collection of EC2 instances that share similar characteristics and are treated as a logical grouping for the purposes of fleet management and dynamic scaling. For example, if a single application operates across multiple instances, you might want to increase the number of instances in that group to improve the performance of the application, or decrease the number of instances to reduce costs when demand is low.
Amazon EC2 Auto Scaling will automaticallly adjust the number of instances in the group to maintain a fixed number of instances even if a instance becomes unhealthy, or based on criteria that you specify. Amazon SNS coordinates and manages the delivery or sending of notifications to subscribing clients or endpoints. This email contains the details of the terminated instance, such as the instance ID and the reason that the instance was terminated. When you create a launch configuration, you specify information for the instances such as the ID of the Amazon Machine Image AMIthe instance type, a key pair, one or more security groups, and a block device mapping.
If you've launched an EC2 instance before, you specified the same information in order to launch the instance. When you create an EC2 Auto Scaling group, you must specify a launch configuration.
You can specify your launch configuration with multiple EC2 Auto Scaling groups. However, you can only specify one launch configuration for an EC2 Auto Scaling group at a time, and you can't modify a launch configuration after you've created it.
Therefore, if you want to change the launch configuration for your EC2 Auto Scaling group, you must create a launch configuration and then update your EC2 Auto Scaling group with the new launch configuration. When you change the launch configuration for your EC2 Auto Scaling group, any new instances are launched using the new configuration parameters, but existing instances are not affected.
Q: What happens if a scaling activity causes me to reach my Amazon EC2 limit of instances? EC2 Auto Scaling groups are regional constructs. Q: If I have data installed in an EC2 Auto Scaling group, and a new instance is dynamically created later, is the data copied over to the new instances? Data is not automatically copied from existing instances to new instances. You can use lifecycle hooks to copy the data, or an Amazon RDS database including replicas.
Balancing resources across Availability Zones is a best practice for well-architected applications, as this greatly increases aggregate system availability. Amazon EC2 Auto Scaling always launches new instances such that they are balanced between zones as evenly as possible across the entire fleet. Lifecycle hooks let you take action before an instance goes into service or before it gets terminated.
One way to do this is by connecting the launch hook to an AWS Lambda function that invokes RunCommand on the instance. Terminate hooks can be useful for collecting important data from an instance before it goes away. An unhealthy instance is one where the hardware has become impaired for some reason bad disk, etc.Autoscalingalso spelled auto scaling or auto-scalingand sometimes also called automatic scalingis a method used in cloud computingwhereby the amount of computational resources in a server farm, typically measured in terms of the number of active servers, which vary automatically based on the load on the farm.
Typically this means the number of servers you pay for goes up or down as users are busy or quiet on your web servers. It is closely related to, and builds upon, the idea of load balancing.
Autoscaling differs from having a fixed daily, weekly, or yearly cycle of server use in that it is responsive to actual usage patterns, and thus reduces the potential downside of having too few or too many servers for the traffic load. For instance, if traffic is usually lower at midnight, then a static scaling solution might schedule some servers to sleep at night, but this might result in downtime on a night where people happen to use the Internet more for instance, due to a viral news event.
Autoscaling, on the other hand, can handle unexpected traffic spikes better. Amazon Web Services launched the Amazon Elastic Compute Cloud EC2 service in Augustthat allowed developers to programmatically create and terminate instances machines.
Third-party autoscaling software for AWS began appearing around April These included tools by Scalr  and RightScale. RightScale was used by Animoto, which was able to handle Facebook traffic by adopting autoscaling. On-demand video provider Netflix documented their use of autoscaling with Amazon Web Services to meet their highly variable consumer needs. They found that aggressive scaling up and delayed and cautious scaling down served their goals of uptime and responsiveness best.
Various best practice guides for AWS use suggest using its autoscaling feature even in cases where the load is not variable.
That is because autoscaling offers two other advantages: automatic replacement of any instances that become unhealthy for any reason such as hardware failure, network failure, or application errorand automatic replacement of spot instances that get interrupted for price or capacity reasons, making it more feasible to use spot instances for production purposes.
On June 27,Microsoft announced that it was adding autoscaling support to its Windows Azure cloud computing platform. Oracle Cloud Platform allows server instances to automatically scale a cluster in or out by defining an auto-scaling rule.
On November 17,the Google Compute Engine announced a public beta of its autoscaling feature for use in Google Cloud Platform applications. In a blog post in Augusta Facebook engineer disclosed that the company had started using autoscaling to bring down its energy costs.
Kubernetes Horizontal Pod Autoscaler automatically scales the number of pods in a replication controllerdeployment or replicaset based on observed CPU utilization or, with beta support, on some other, application-provided metrics . Autoscaling by default uses reactive decision approach for dealing with traffic scaling: scaling only happens in response to real-time changes in metrics.Speech pathology internships summer 2020 chicago
In some cases, particularly when the changes occur very quickly, this reactive approach to scaling is insufficient. Two other kinds of autoscaling decision approaches are described below. This is an approach to autoscaling where changes are made to the minimum size, maximum size, or desired capacity of the autoscaling group at specific times of day. Scheduled scaling is useful, for instance, if there is a known traffic load increase or decrease at specific times of the day, but the change is too sudden for reactive approach based autoscaling to respond fast enough.
AWS autoscaling groups support scheduled scaling. This approach to autoscaling uses predictive analytics.Bexar county low income housing
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have one scale in simple policy in my autoscaling group which is based on CPU Utilization. The policy looks like:. The problem is I cannot use scaling policy with steps since the cooldown time is not supported which could make my asg scaling in until the min instances.
And if I have both simple policies, they are obviously conflict. You would certainly need to use Scaling Policy with Steps to be able to specify multiple rules for the scaling policy.
While it doesn't allow the specification of a Cooldown period, it should work fine. By the way, you have a very aggressive policy. It is not typically a good idea to scale-in based upon only 5 minutes of data. Amazon EC2 is charged in hourly increments, so you might be thrashing adding and removing instances very quicklywhich is not economical. It is typically recommended to scale-out quickly to respond to user demand but scale-in slowly since there's really no rush.
Learn more. Asked 2 years, 10 months ago. Active 2 years, 10 months ago. Viewed times. Does anyone have a workaround of this one? Liu Liu 8 8 silver badges 16 16 bronze badges. Can you please share what goal are you trying to achieve? KunalPradhan goal is added in the question. Active Oldest Votes. John Rotenstein John Rotenstein k 9 9 gold badges silver badges bronze badges. Unfortunately the policy with steps doesn't work for scaling in because of the cooldown time. I made a test and the scale in policy was triggered one after another until it reached the min instances set in the asg.
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Step and Simple Scaling Policies for Amazon EC2 Auto Scaling
Thanks for letting us know this page needs work. We're sorry we let you down. If you've got a moment, please tell us how we can make the documentation better. With IAM identity-based policies, you can specify allowed or denied actions and resources, and the conditions under which actions are allowed or denied.
Amazon EC2 Auto Scaling supports specific actions, resources, and condition keys. The Action element of an IAM identity-based policy describes the specific action or actions that will be allowed or denied by the policy.Spyon component variable
The action is used in a policy to grant permissions to perform the associated operation. Policy statements must include either an Action or NotAction element. Amazon EC2 Auto Scaling defines its own set of actions that describe tasks that you can perform with this service. To specify multiple actions in a single statement, separate them with commas as shown in the following example. For example, to specify all actions that begin with the word Describeinclude the following action.
The Resource element specifies the object or objects to which the action applies. Statements must include either a Resource or a NotResource element. You can restrict access to specific Auto Scaling groups and launch configurations by using their ARNs to identify the resource that the IAM policy applies to. The Condition element or Condition block lets you specify conditions in which a statement is in effect. The Condition element is optional.
You can build conditional expressions that use condition operatorssuch as equals or less than, to match the condition in the policy with values in the request. If you specify multiple Condition elements in a statement, or multiple keys in a single Condition element, AWS evaluates them using a logical AND operation. If you specify multiple values for a single condition key, AWS evaluates the condition using a logical OR operation. All of the conditions must be met before the statement's permissions are granted.
You can also use placeholder variables when you specify conditions. Amazon EC2 Auto Scaling defines its own set of condition keys and also supports using some global condition keys. You can apply tag-based, resource-level permissions in the identity-based policies that you create for Amazon EC2 Auto Scaling.
This gives you better control over which resources a user can create, modify, use, or delete.Did you find this page useful? Do you have a suggestion? Give us feedback or send us a pull request on GitHub. See the User Guide for help getting started.
See 'aws help' for descriptions of global parameters. Specifies whether the ScalingAdjustment parameter is an absolute number or a percentage of the current capacity. Valid only if the policy type is StepScaling or SimpleScaling.
The minimum number of instances to scale. Otherwise, the error is ValidationError.Marbles game getter collector
This property replaces the MinAdjustmentStep property. For example, suppose that you create a step scaling policy to scale out an Auto Scaling group by 25 percent and you specify a MinAdjustmentMagnitude of 2.
If the group has 4 instances and the scaling policy is performed, 25 percent of 4 is 1. Valid only if the policy type is SimpleScaling or StepScaling.
The amount by which a simple scaling policy scales the Auto Scaling group in response to an alarm breach. The adjustment is based on the value that you specified in the AdjustmentType parameter either an absolute number or a percentage.
A positive value adds to the current capacity and a negative value subtracts from the current capacity. For exact capacity, you must specify a positive value.
Conditional: If you specify SimpleScaling for the policy type, you must specify this parameter. Not used with any other policy type. The amount of time, in seconds, after a scaling activity completes before any further dynamic scaling activities can start. If this parameter is not specified, the default cooldown period for the group applies.
Valid only if the policy type is SimpleScaling. The aggregation type for the CloudWatch metrics. The valid values are MinimumMaximumand Average. If the aggregation type is null, the value is treated as Average.If you've got a moment, please tell us what we did right so we can do more of it. Thanks for letting us know this page needs work. We're sorry we let you down. If you've got a moment, please tell us how we can make the documentation better.
When you configure dynamic scaling, you define how to scale the capacity of your Auto Scaling group in response to changing demand. For example, let's say that you have a web application that currently runs on two instances, and you want the CPU utilization of the Auto Scaling group to stay at around 50 percent when the load on the application changes. This gives you extra capacity to handle traffic spikes without maintaining an excessive number of idle resources.
You can configure your Auto Scaling group to scale dynamically to meet this need by creating a scaling policy. Amazon EC2 Auto Scaling can then scale out your group add more instances to deal with high demand at peak times, and scale in your group run fewer instances to reduce costs during periods of low utilization. The metrics that are used to trigger an alarm are an aggregation of metrics coming from all of the instances in the Auto Scaling group.
On average, they are at 50 percent CPU. When the policy is in effect, Amazon EC2 Auto Scaling adjusts the group's desired capacity up or down when the alarm is triggered. When a scaling policy is executed, if the capacity calculation produces a number outside of the minimum and maximum size range of the group, Amazon EC2 Auto Scaling ensures that the new capacity never goes outside of the minimum and maximum size limits. Capacity is measured in one of two ways: using the same units that you chose when you set the desired capacity in terms of instances, or using capacity units if instance weighting is applied.
Example 1: An Auto Scaling group has a maximum capacity of 3, a current capacity of 2, and a scaling policy that adds 3 instances. When executing this scaling policy, Amazon EC2 Auto Scaling adds only 1 instance to the group to prevent the group from exceeding its maximum size.
Example 2: An Auto Scaling group has a minimum capacity of 2, a current capacity of 3, and a scaling policy that removes 2 instances. When executing this scaling policy, Amazon EC2 Auto Scaling removes only 1 instance from the group to prevent the group from becoming less than its minimum size.
When the desired capacity reaches the maximum size limit, scaling out stops. The exception is when you have two or more instance types in a group and use instance weighting. In this case, Amazon EC2 Auto Scaling can scale out above the maximum size limit, but only by up to your maximum instance weight. Its intention is to get as close to the new desired capacity as possible but still adhere to the allocation strategies that are specified for the group.
The allocation strategies determine which instance pools your instances come from. The weights determine how many capacity units each instance contributes to the capacity of the group.
The heavier the weight, the higher the number. Example 3: An Auto Scaling group has a maximum capacity of 12, a current capacity of 10, and a scaling policy that adds 5 capacity units. Instance types have one of three weights assigned: 1, 4, or 6. When executing the scaling policy, Amazon EC2 Auto Scaling chooses to launch an instance type with a weight of 6 based on the allocation strategy.
The result of this scale-out event is a group with a desired capacity of 12 and a current capacity of Target tracking scaling —Increase or decrease the current capacity of the group based on a target value for a specific metric. This is similar to the way that your thermostat maintains the temperature of your home—you select a temperature and the thermostat does the rest. Step scaling —Increase or decrease the current capacity of the group based on a set of scaling adjustments, known as step adjustmentsthat vary based on the size of the alarm breach.
Simple scaling —Increase or decrease the current capacity of the group based on a single scaling adjustment. If you are scaling based on a utilization metric that increases or decreases proportionally to the number of instances in an Auto Scaling group, we recommend that you use target tracking scaling policies.
Otherwise, we recommend that you use step scaling policies. In most cases, a target tracking scaling policy is sufficient to configure your Auto Scaling group to scale out or scale in automatically. A target tracking scaling policy allows you to select a desired outcome and have the Auto Scaling group add and remove instances as needed to achieve that outcome.
For an advanced scaling configuration, your Auto Scaling group can have more than one scaling policy. For example, you can define one or more target tracking scaling policies, one or more step scaling policies, or both.If you've got a moment, please tell us what we did right so we can do more of it.
Thanks for letting us know this page needs work. We're sorry we let you down. If you've got a moment, please tell us how we can make the documentation better. With step scaling and simple scaling, you choose scaling metrics and threshold values for the CloudWatch alarms that trigger the scaling process.
You also define how your Auto Scaling group should be scaled when a threshold is in breach for a specified number of evaluation periods. We strongly recommend that you use a target tracking scaling policy to scale on a metric like average CPU utilization or the RequestCountPerTarget metric from the Application Load Balancer. Metrics that decrease when capacity increases and increase when capacity decreases can be used to proportionally scale out or in the number of instances using target tracking.
This helps ensure that Amazon EC2 Auto Scaling follows the demand curve for your applications closely. For more information, see Target Tracking Scaling Policies. You still have the option to use step scaling as an additional policy for a more advanced configuration.
For example, you can configure a more aggressive response when demand reaches a certain level. Step scaling policies and simple scaling policies are two of the dynamic scaling options available for you to use.
Both require you to create CloudWatch alarms for the scaling policies. Both require you to specify the high and low thresholds for the alarms. Both require you to define whether to add or remove instances, and how many, or set the group to an exact size. The main difference between the policy types is the step adjustments that you get with step scaling policies.
When step adjustments are applied, and they increase or decrease the current capacity of your Auto Scaling group, the adjustments vary based on the size of the alarm breach. In most cases, step scaling policies are a better choice than simple scaling policies, even if you have only a single scaling adjustment. The main issue with simple scaling is that after a scaling activity is started, the policy must wait for the scaling activity or health check replacement to complete and the cooldown period to expire before responding to additional alarms.
Cooldown periods help to prevent the initiation of additional scaling activities before the effects of previous activities are visible. In contrast, with step scaling the policy can continue to respond to additional alarms, even while a scaling activity or health check replacement is in progress.
Therefore, all alarms that are breached are evaluated by Amazon EC2 Auto Scaling as it receives the alarm messages.
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