AWS Systems Manager Sub-Services Overview

This document provides an overview of the various sub-services available within AWS Systems Manager for operational management and monitoring.

AWS Systems Manager Sub-Services

Explorer

View an aggregated dashboard of operational data across AWS services.

OpsCenter

Centralize operational issues (OpsItems) for diagnosis and resolution.

CloudWatch Dashboard

Monitor and visualize metrics across multiple AWS services in real-time.

Incident Manager

Automate incident response plans for faster resolution of critical issues.

Application Manager

Manage and visualize your application resources in one centralized location.

AppConfig

Safely deploy configuration changes to applications across environments.

Parameter Store

Securely store and manage configuration data and secrets.

Change Manager

Automate and approve changes in a controlled, auditable manner.

Automation:

Automate common maintenance tasks such as restarting instances.

Change Calendar:

Schedule changes to avoid conflicts during critical periods.

Maintenance Windows:

Define time periods for maintenance tasks like patching or updates.

Fleet Manager:

Simplify the management of EC2 instances and servers from a single console.

Compliance:

Check if instances comply with security policies and desired configurations.

Inventory:

Track and manage installed software and configurations on instances.

Hybrid Activations:

Manage on-premises servers and VMs as if they were AWS instances.

Session Manager:

Securely connect to and manage EC2 instances without the need for SSH.

Run Command:

Remotely execute commands across your fleet of instances.

State Manager:

Define and enforce desired configurations on your instances.

Patch Manager:

Automate patching of instances to keep them updated.

Distributor:

Distribute software packages and updates to your instances.

Documents:

Create and manage SSM documents to define actions for your instances.

Auto Scaling

Manual Scaling: Manually adjust the number of running instances based on real-time needs.

Scheduled Scaling: Automatically scale resources at specific times to handle predictable workloads.

Dynamic Scaling: Automatically scale based on demand using metrics such as CPU utilization or network traffic.

Target Tracking Scaling: Maintain a specific target (e.g., 50% CPU utilization) by automatically adjusting resources.

Step and Simple Scaling Policies: Increase or decrease capacity in steps based on specific thresholds.

Scaling Policy Based on Amazon SQS: Scale based on the number of messages in an Amazon SQS queue.

Predictive Scaling: Use machine learning to predict future traffic and automatically scale in advance.

Scaling Cooldowns: Avoid scaling up or down too quickly by setting cool-down periods between scaling actions.