Implementing automated alarms by using CloudWatch

What is Amazon CloudWatch, and how is it pertinent to network monitoring on AWS?

Amazon CloudWatch is a monitoring and observability service offered by AWS that provides data and actionable insights to monitor applications, respond to system-wide performance changes, optimize resource utilization, and get a unified view of the operational health of AWS resources, applications, and services that run on AWS and on-premises servers. CloudWatch is pertinent to network monitoring on AWS as it can collect and track metrics, collect and monitor log files, set alarms, and automatically react to changes in AWS resources such as EC2 instances and load balancers.

How would you set up an automated alarm in CloudWatch for a high network in throughput scenario?

To set up an automated alarm in CloudWatch for high network throughput, you would:

• Navigate to the CloudWatch console.
• Choose ‘Alarms’ from the navigation pane, and click ‘Create Alarm’.
• Select the metric for network input/output, such as ‘NetworkIn’ or ‘NetworkOut’ for the appropriate EC2 instance or network interface.
• Set the threshold that signifies “high” network throughput based on your requirements.
• Configure actions to notify you or trigger automated responses using Amazon SNS or Auto Scaling policies when the alarm state is reached.
• Finally, name and describe the alarm for future reference and confirm the alarm creation.

What types of automated actions can CloudWatch alarms trigger when a threshold is breached?

CloudWatch alarms can trigger a variety of automated actions when a threshold is breached, including:

• Sending notifications using Amazon Simple Notification Service (Amazon SNS).
• Stopping, terminating, rebooting, or recovering an EC2 instance.
• Launching or terminating EC2 instances when used with Auto Scaling groups.
• Adjusting the desired capacity of Auto Scaling groups.
• Running an AWS Lambda function for custom actions.
• Publishing to an Amazon SQS queue or an Amazon Kinesis Data Firehose stream.

Can CloudWatch alarms be used to monitor for network connectivity issues, and if so, how?

Yes, CloudWatch alarms can monitor network connectivity issues by setting alarms on metrics that reflect such conditions. For instance, you can create an alarm based on the `StatusCheckFailed_System` metric, which indicates underlying problems with your EC2 instances, such as network connectivity. You could also set alarms on metrics like ‘NetworkPacketsIn’ or ‘NetworkPacketsOut’ and look for unusual drops to zero or values well below typical thresholds, which may signify connectivity issues.

What is the difference between CloudWatch metrics granularity for standard and detailed monitoring, and how does this affect alarm setup?

Standard monitoring in CloudWatch for EC2 instances provides metric data in 5-minute periods, while detailed monitoring provides data in 1-minute periods. The granularity affects alarm setup since the higher resolution of detailed monitoring can lead to faster alarm notifications and potentially more precise scaling and response activities. However, detailed monitoring incurs additional costs.

Is it possible to aggregate CloudWatch metrics across multiple resources to trigger a single alarm, and what is the method for doing so?

Yes, it is possible to aggregate CloudWatch metrics across multiple resources to trigger a single alarm by using metric math expressions to create new time series based on a combination of existing metrics. This is done by enabling the ‘Across All Instances’ feature when selecting the metric for the alarm, which allows you to aggregate metrics for resources such as EC2 instances based on the chosen statistic (e.g., average, sum).

Explain how you would configure a CloudWatch alarm to notify an operations team whenever network latency exceeds a threshold?

To configure a CloudWatch alarm for network latency, you’d:

• Go to the CloudWatch console and create a new alarm.
• Choose the relevant metric associated with network latency like ‘NetworkRoundTripTime’ if you’ve got custom metrics or a suitable proxy metric available.
• Set the threshold above which network latency is considered to be excessive for your application’s requirements.
• Define the period over which the metric is evaluated.
• Choose the action of sending a notification to an Amazon SNS topic that the operations team is subscribed to.
• Provide the name and description for the alarm and complete its setup.

Discuss how you could ensure a CloudWatch alarm triggers an action for sustained high CPU utilization rather than a temporary spike on an EC2 instance.

To ensure an alarm is triggered by sustained high CPU utilization rather than a temporary spike, you would set up the alarm with the following parameters:

• Increasing the ‘period’ over which the CPU utilization metric is measured to a longer duration.
• Adjusting the ‘datapoints to alarm’ setting to require multiple consecutive periods of high CPU utilization before changing the alarm state.
• Selecting an appropriate ‘statistic’, such as average, to smooth out short-term fluctuations.
• Defining an ‘evaluation period’ long enough to consider persistent high usage.

How would you use CloudWatch to automate the scaling of an EC2 fleet in response to changing network conditions?

To use CloudWatch to automate the scaling of an EC2 fleet based on network conditions, you would:

• Create CloudWatch alarms based on network metrics such as ‘NetworkIn’ or ‘NetworkOut’.
• Link the alarms to corresponding Auto Scaling policies that either increase or decrease the desired capacity of the EC2 fleet.
• The Auto Scaling group then interprets these policies to launch or terminate instances dynamically, ensuring that the EC2 fleet scales in response to the actual network load.

What are some best practices for setting thresholds for CloudWatch alarms, particularly for network-related events?

Best practices for setting thresholds for CloudWatch alarms include:

• Understanding the normal baseline for network metrics under typical and peak conditions to set realistic thresholds.
• Employing a strategy that factors in both absolute and relative thresholds (e.g., setting a threshold at a percentage above the normal level).
• Avoid setting thresholds too close to predicted peak values to prevent noisy alarms for expected behavior.
• Regularly reviewing and adjusting the thresholds as traffic patterns and usage change over time.
• Utilizing anomaly detection models where applicable to dynamically adjust thresholds based on learned patterns.