Excessive availability patterns for AWS IoT Greengrass utilizing Pacemaker

Edge computing downtime in industrial IoT environments may be each inconvenient and expensive. Methods on the edge require steady operation to keep up enterprise continuity. Whereas AWS IoT Greengrass delivers highly effective edge computing capabilities, reaching true enterprise-grade excessive availability requires further orchestration. This submit reveals the best way to use Pacemaker, a cluster useful resource supervisor, to construct resilient edge infrastructure with automated failover.

On this walkthrough, you’ll study to implement energetic/passive and energetic/energetic excessive availability patterns utilizing Pacemaker with AWS IoT Greengrass, full with automated failover, state replication, and monitoring integration.

The excessive availability problem for edge computing

Conventional cloud purposes profit from built-in redundancy and auto-scaling, nonetheless, purposes on the sting face distinctive challenges:

• Bodily isolation: Edge units function in distant areas with restricted connectivity
• Useful resource constraints: Not like cloud environments, edge assets are finite and treasured
• Service criticality: Edge failures can halt bodily operations instantly
• Restoration complexity: Guide intervention at distant websites is pricey and gradual

AWS IoT Greengrass addresses many edge computing challenges, however excessive availability requires considerate structure past a single gadget deployment.

How Pacemaker enhances AWS IoT Greengrass

Pacemaker helps you construct extremely obtainable AWS IoT Greengrass deployments by means of cluster administration capabilities:

Confirmed reliability

• Utilized in mission-critical environments for over a decade
• Handles complicated failure situations with subtle fencing mechanisms
• Works in each energetic/passive and energetic/energetic configurations

AWS IoT Greengrass-aware useful resource administration

• Displays Greengrass service well being and element states
• Manages shared storage for seamless state switch
• Coordinates failover of dependent providers and community assets

Enterprise-ready integration

• Integrates with present Linux infrastructure administration
• Helps complicated dependency chains and useful resource constraints
• Supplies detailed logging and monitoring for compliance necessities

Collectively, these instruments hold your edge workloads working throughout {hardware} failures or community disruptions.

Structure overview: Excessive availability patterns

AWS IoT Greengrass excessive availability may be applied utilizing two major patterns, every optimized for various use instances.

Lively/Passive configuration: Maximizing information consistency

This mode maximizes information consistency and automatic failover—preferrred for mission-critical purposes the place information integrity and repair continuity are paramount. One node runs Greengrass actively whereas the opposite stands prepared in standby mode. A software-based, block-level information replication service like Distributed Replicated Block Gadget (DRBD) ensures on the spot state synchronization between nodes, enabling failover with zero information loss and sustaining gadget id.

┌─────────────────┐    ┌─────────────────┐ 
│   Major Node  │    │  Standby Node   │ 
│                 │    │                 │ 
│ ┌─────────────┐ │    │ ┌─────────────┐ │ 
│ │ Greengrass  │ │    │ │ Greengrass  │ │ 
│ │   ACTIVE    │ │    │ │  STANDBY    │ │ 
│ └─────────────┘ │    │ └─────────────┘ │ 
│                 │    │                 │ 
│ ┌─────────────┐ │    │ ┌─────────────┐ │ 
│ │   DRBD      │◄┼────┼►│   DRBD      │ │ 
│ │  Major    │ │    │ │ Secondary   │ │ 
│ └─────────────┘ │    │ └─────────────┘ │ 
└─────────────────┘    └─────────────────┘ 

Key advantages:

This configuration ensures full state preservation throughout failover with sub-minute downtime, zero information loss for in-flight transactions and important operations, whereas sustaining gadget id, certificates, and Stream Supervisor persistence seamlessly.

Actual-world use instances:

Lively/Passive configurations are important in situations requiring zero or minimal information loss, akin to in-flight leisure programs that deal with offline cost processing and battery manufacturing services the place manufacturing traces rely upon steady information move from vital manufacturing sensors and ML mannequin outputs to keep up operational integrity and high quality management.

Lively/Lively: Most throughput and scalability

This mode maximizes throughput and supplies horizontal scaling for high-volume workloads. A number of impartial Greengrass situations run concurrently throughout cluster nodes, with clever load balancing distributing work primarily based on node well being and capability. Every node operates with its personal distinctive gadget credentials and configurations.

┌─────────────────┐    ┌─────────────────┐ 
│   Node 1        │    │   Node 2        │ 
│                 │    │                 │ 
│ ┌─────────────┐ │    │ ┌─────────────┐ │ 
│ │ Greengrass  │ │    │ │ Greengrass  │ │ 
│ │   ACTIVE    │ │    │ │   ACTIVE    │ │ 
│ └─────────────┘ │    │ └─────────────┘ │ 
│                 │    │                 │ 
│ ┌─────────────┐ │    │ ┌─────────────┐ │ 
│ │Load Balancer│◄┼────┼►│Load Balancer│ │ 
│ │ (A/P Mode)  │ │    │ │ (Standby)   │ │ 
│ └─────────────┘ │    │ └─────────────┘ │ 
└─────────────────┘    └─────────────────┘ 

Key advantages:

These configurations allow horizontal scaling for high-throughput situations, enhance useful resource utilization throughout nodes, and supply sleek degradation beneath partial failures.

Actual-world use instances:

Lively/Lively configurations are perfect for high-volume situations akin to automotive elements manufacturing services and large-scale manufacturing operations with a number of manufacturing traces, the place every node handles completely different line segments to supply each redundancy and elevated processing capability for real-time analytics and anomaly detection.

Configuration choice information

Use Lively/Passive for purposes that require zero information loss, shared state, and gadget id preservation. This sample works properly once you want a single level of management and might settle for failover occasions beneath one minute.Use Lively/Lively once you want excessive throughput and horizontal scaling. This sample fits purposes that may function independently with out shared state, the place load distribution supplies operational advantages, and sleek degradation is preferable to finish failover.

The right way to implementation the answer

The whole playbook, together with detailed configuration examples and testing procedures, is out there within the GitHub respository. This supplies an Lively/Passive implementation automation utilizing Ansible that you would be able to customise in your particular necessities. Lively/Lively setup steps are additionally obtainable in MANUAL-SETUP-GUIDE inside the similar repository.

Setup steps

1. Atmosphere setup

Clone the repository and arrange the event atmosphere

git clone https://github.com/aws-samples/sample-greengrass-ha-pacemaker.git
cd sample-greengrass-ha-pacemaker
./scripts/setup-dev-env.sh && supply .venv/bin/activate

2. Configure cluster secrets and techniques

Generate and encrypt cluster credentials utilizing Ansible Vault

# Create vault password file
echo "your_secure_password" > .vault_pass
chmod 600 .vault_pass
# Auto-generate encrypted secrets and techniques
./scripts/setup-vault.sh

This creates `vars/cluster-vault.yml` with encrypted credentials for cluster authentication and DRBD replication.

3. Put together Greengrass credentials

Notice: This method is designed for testing and demonstration functions solely.

Obtain Greengrass set up information from AWS IoT Console.

1. Navigate to AWS IoT Core console → Greengrass → Core units
2. Click on ‘Arrange one core gadget’ → ‘Arrange a tool with installer obtain’
3. Title your gadget (e.g., ‘greengrass-ha-device’)
4. Choose or create a Factor Group
5. Obtain each information and rename them:
   1. Rename hash-setup.sh to greengrass-setup.sh
   2. Rename hash.zip to greengrass-certs.zip
6. Place information in `information/greengrass/` listing

4. Deploy and configure

This can deploy AWS EC2 and crucial assets to check on AWS.

# Deploy infrastructure
make cdk-deploy && make cdk-inventory
# Retrieve SSH personal key
./scripts/get-ssh-key.sh
# Configure HA cluster
ansible-playbook playbooks/setup/system-prerequisites.yml -i stock/cdk-dev-hosts
ansible-playbook playbooks/setup/configure-ha.yml -i stock/cdk-dev-hosts --vault-password-file .vault_pass

5. Validate and take a look at

Verify cluster standing and optionally, run an automatic failover take a look at.

# Verify cluster standing
ansible node-1 -i stock/cdk-dev-hosts -m shell -a "sudo pcs standing" --become
# Take a look at failover (non-compulsory)
ansible-playbook playbooks/testing/test-failover-simulation.yml -i stock/cdk-dev-hosts --vault-password-file .vault_pass

The automated checks validate useful resource migration, DRBD promotion, and information consistency throughout failover.

Cleanup

This can destroy the assets created by CDK.

# Destroy infrastructure
make cdk-destroy

Conclusion: Enterprise-ready edge computing

AWS IoT Greengrass and Pacemaker collectively present the excessive availability wanted for mission-critical edge deployments. By utilizing Pacemaker’s cluster administration capabilities, organizations can confidently deploy Greengrass the place reliability is crucial.Whether or not you’re managing industrial management programs, processing real-time analytics, or orchestrating edge AI workloads, this architectural sample supplies the muse for resilient, scalable edge computing that your enterprise can rely upon.

Subsequent steps

Able to implement enterprise-grade excessive availability in your AWS IoT Greengrass deployments? Right here’s your path ahead:

Repository: sample-greengrass-ha-pacemaker

Concerning the authors

Yong Ji Yong Ji is a Senior Options Architect at Amazon Internet Companies (AWS), serving to enterprises construct modern cloud-based options. With over 25 years of expertise in cloud structure, analytics and information engineering, Yong brings deep technical experience and a ardour for fixing complicated enterprise challenges. Exterior of labor, Yong is a passionate desk tennis participant.

Siddhant Srivastava Siddhant Srivastava is a Software program Improvement Engineer with AWS IoT Greengrass. He has 3+ years of expertise in edge computing with give attention to constructing resilient, scalable distributed programs. Exterior work, Siddhant participates in soccer leagues and billiards tournaments.