Need help?

Our experts have had an average response time of 12.14 minutes in September 2021 to fix urgent issues.

We will keep your servers stable, secure, and fast at all times for one fixed price.

Create an Amazon CloudWatch alarm based on anomaly detection

by | Aug 25, 2021

Looking for how to create a CloudWatch alarm based on anomaly detection? We can help you!

As a part of our AWS Support Services, we often receive similar requests from our AWS customers.

Today, let’s see the steps followed by our Support Techs to help our customers with the creation of the Amazon CloudWatch alarm.

 

CloudWatch alarm based on anomaly detection

 
Amazon CloudWatch is a monitoring and observability service from AWS. ‘anomaly detection’ is a feature of CloudWatch that uses Machine Learning to automate the creation of alarms and their thresholds.

When we enable anomaly detection for a metric, CloudWatch applies machine-learning algorithms to the metric’s historical data to create a model of the metric’s expected values.

The model generates two metrics and it represents the upper band of normal metric behavior and the lower band of normal metric behavior, with a default value of two standard deviations.

  1. Firstly we need to create a JSON file to set a CloudWatch alarm based on anomaly detection.
{
"AlarmActions": [
"arn:aws:sns:us-east-1:397466294846:test1"
],
"AlarmName": "MyAlarmName",
"AlarmDescription": "This alarm uses an anomaly detection model",
"Metrics": [
{
"Id": "m1",
"ReturnData": true,
"MetricStat": {
"Metric": {
"MetricName": "NetworkIn",
"Namespace": "AWS/EC2",
"Dimensions": [
{
"Name": "InstanceId",
"Value": "i-0e1830cdc0447f6b9"
}
]
},
"Stat": "Average",
"Period": 60
}
},
{
"Id": "t1",
"Expression": "ANOMALY_DETECTION_BAND(m1, 3)"
}
],
"EvaluationPeriods": 2,
"ThresholdMetricId": "t1",
"ComparisonOperator": "LessThanLowerOrGreaterThanUpperThreshold"
}

Here the Id of m1 is assigned to the NetworkIn metric of an instance. t1 is the anomaly detection model function for the NetworkIn metric. The model uses three standard deviations to set the width of the band.

ThresholdMetricId is set to t1, and ComparisonOperator is set to LessThanLowerOrGreaterThanUpperThreshold.

This will ensure that the alarm goes into an alarm state when the metric value is outside the anomaly model band in two consecutive evaluation periods.

2. Then save the JSON file as anomaly-alarm.json.

3. To create an alarm with the anomaly detection band specified in the file, run the following command:

$ aws cloudwatch put-metric-alarm --cli-input-json file://anomaly-alarm.json

The model will generate when we finish creating the alarm.

[Need help with more AWS queries? We’d be happy to assist]
 

Conclusion

 
To conclude, today we discussed the steps followed by our Support Engineers to help our customers to create a CloudWatch alarm based on anomaly detection using AWS CLI.

PREVENT YOUR SERVER FROM CRASHING!

Never again lose customers to poor server speed! Let us help you.

Our server experts will monitor & maintain your server 24/7 so that it remains lightning fast and secure.

GET STARTED

var google_conversion_label = "owonCMyG5nEQ0aD71QM";

0 Comments

Submit a Comment

Your email address will not be published. Required fields are marked *

Privacy Preference Center

Necessary

Necessary cookies help make a website usable by enabling basic functions like page navigation and access to secure areas of the website. The website cannot function properly without these cookies.

PHPSESSID - Preserves user session state across page requests.

gdpr[consent_types] - Used to store user consents.

gdpr[allowed_cookies] - Used to store user allowed cookies.

PHPSESSID, gdpr[consent_types], gdpr[allowed_cookies]
PHPSESSID
WHMCSpKDlPzh2chML

Statistics

Statistic cookies help website owners to understand how visitors interact with websites by collecting and reporting information anonymously.

_ga - Preserves user session state across page requests.

_gat - Used by Google Analytics to throttle request rate

_gid - Registers a unique ID that is used to generate statistical data on how you use the website.

smartlookCookie - Used to collect user device and location information of the site visitors to improve the websites User Experience.

_ga, _gat, _gid
_ga, _gat, _gid
smartlookCookie

Marketing

Marketing cookies are used to track visitors across websites. The intention is to display ads that are relevant and engaging for the individual user and thereby more valuable for publishers and third party advertisers.

IDE - Used by Google DoubleClick to register and report the website user's actions after viewing or clicking one of the advertiser's ads with the purpose of measuring the efficacy of an ad and to present targeted ads to the user.

test_cookie - Used to check if the user's browser supports cookies.

1P_JAR - Google cookie. These cookies are used to collect website statistics and track conversion rates.

NID - Registers a unique ID that identifies a returning user's device. The ID is used for serving ads that are most relevant to the user.

DV - Google ad personalisation

IDE, test_cookie, 1P_JAR, NID, DV, NID
IDE, test_cookie
1P_JAR, NID, DV
NID
hblid

Security

These are essential site cookies, used by the google reCAPTCHA. These cookies use an unique identifier to verify if a visitor is human or a bot.

SID, APISID, HSID, NID, PREF
SID, APISID, HSID, NID, PREF