{"id":17468,"date":"2025-11-16T06:16:39","date_gmt":"2025-11-15T21:16:39","guid":{"rendered":"https:\/\/aireviewirush.com\/?p=17468"},"modified":"2025-11-16T06:16:39","modified_gmt":"2025-11-15T21:16:39","slug":"excessive-availability-patterns-for-aws-iot-greengrass-utilizing-pacemaker","status":"publish","type":"post","link":"https:\/\/aireviewirush.com\/?p=17468","title":{"rendered":"Excessive availability patterns for AWS IoT Greengrass utilizing Pacemaker"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"\">\n<p>Edge computing downtime in industrial IoT environments will be each inconvenient and dear. Methods on the edge require steady operation to keep up enterprise continuity. Whereas <a href=\"https:\/\/aws.amazon.com\/greengrass\/\" target=\"_blank\" rel=\"noopener noreferrer\">AWS IoT Greengrass<\/a> delivers highly effective edge computing capabilities, attaining true enterprise-grade excessive availability requires further orchestration. This put up exhibits the way to use <a href=\"https:\/\/github.com\/ClusterLabs\/pacemaker\" target=\"_blank\" rel=\"noopener noreferrer\">Pacemaker<\/a>, a cluster useful resource supervisor, to construct resilient edge infrastructure with automated failover.<\/p>\n<p>On this walkthrough, you\u2019ll be taught 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.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_53 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title \" >Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\" role=\"button\"><label for=\"item-69f066f9bd749\" ><span class=\"\"><span style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input aria-label=\"Toggle\" aria-label=\"item-69f066f9bd749\"  type=\"checkbox\" id=\"item-69f066f9bd749\"><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#The_excessive_availability_problem_for_edge_computing\" title=\"The excessive availability problem for edge computing\">The excessive availability problem for edge computing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#How_Pacemaker_enhances_AWS_IoT_Greengrass\" title=\"How Pacemaker enhances AWS IoT Greengrass\">How Pacemaker enhances AWS IoT Greengrass<\/a><ul class='ez-toc-list-level-3'><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Confirmed_reliability\" title=\"Confirmed reliability\">Confirmed reliability<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#AWS_IoT_Greengrass-aware_useful_resource_administration\" title=\"AWS IoT Greengrass-aware useful resource administration\">AWS IoT Greengrass-aware useful resource administration<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Enterprise-ready_integration\" title=\"Enterprise-ready integration\">Enterprise-ready integration<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Structure_overview_Excessive_availability_patterns\" title=\"Structure overview: Excessive availability patterns\">Structure overview: Excessive availability patterns<\/a><ul class='ez-toc-list-level-3'><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#EnergeticPassive_configuration_Maximizing_knowledge_consistency\" title=\"Energetic\/Passive configuration: Maximizing knowledge consistency\">Energetic\/Passive configuration: Maximizing knowledge consistency<\/a><ul class='ez-toc-list-level-4'><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Key_advantages\" title=\"Key advantages:\">Key advantages:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Actual-world_use_instances\" title=\"Actual-world use instances:\">Actual-world use instances:<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#EnergeticEnergetic_Most_throughput_and_scalability\" title=\"Energetic\/Energetic: Most throughput and scalability\">Energetic\/Energetic: Most throughput and scalability<\/a><ul class='ez-toc-list-level-4'><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Key_advantages-2\" title=\"Key advantages:\">Key advantages:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Actual-world_use_instances-2\" title=\"Actual-world use instances:\">Actual-world use instances:<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Configuration_choice_information\" title=\"Configuration choice information\">Configuration choice information<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#How_one_can_implement_the_answer\" title=\"How one can implement the answer\">How one can implement the answer<\/a><ul class='ez-toc-list-level-3'><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Setup_steps\" title=\"Setup steps\">Setup steps<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Cleanup\" title=\"Cleanup\">Cleanup<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Conclusion_Enterprise-ready_edge_computing\" title=\"Conclusion: Enterprise-ready edge computing\">Conclusion: Enterprise-ready edge computing<\/a><ul class='ez-toc-list-level-3'><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#Subsequent_steps\" title=\"Subsequent steps\">Subsequent steps<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/aireviewirush.com\/?p=17468\/#In_regards_to_the_authors\" title=\"In regards to the authors\">In regards to the authors<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"The_excessive_availability_problem_for_edge_computing\"><\/span><strong>The excessive availability problem for edge computing<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Conventional cloud purposes profit from built-in redundancy and auto-scaling, nonetheless, purposes on the sting face distinctive challenges:<\/p>\n<ul>\n<li><strong>Bodily isolation<\/strong>: Edge units function in distant places with restricted connectivity<\/li>\n<li><strong>Useful resource constraints<\/strong>: In contrast to cloud environments, edge assets are finite and valuable<\/li>\n<li><strong>Service criticality<\/strong>: Edge failures can halt bodily operations instantly<\/li>\n<li><strong>Restoration complexity<\/strong>: Handbook intervention at distant websites is pricey and gradual<\/li>\n<\/ul>\n<p>AWS IoT Greengrass addresses many edge computing challenges, however excessive availability requires considerate structure past a single gadget deployment.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_Pacemaker_enhances_AWS_IoT_Greengrass\"><\/span><strong>How Pacemaker enhances AWS IoT Greengrass<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Pacemaker helps you construct extremely accessible AWS IoT Greengrass deployments via cluster administration capabilities:<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Confirmed_reliability\"><\/span><strong>Confirmed reliability<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>Utilized in mission-critical environments for over a decade<\/li>\n<li>Handles advanced failure eventualities with refined fencing mechanisms<\/li>\n<li>Works in each energetic\/passive and energetic\/energetic configurations<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"AWS_IoT_Greengrass-aware_useful_resource_administration\"><\/span><strong>AWS IoT Greengrass-aware useful resource administration<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>Displays Greengrass service well being and element states<\/li>\n<li>Manages shared storage for seamless state switch<\/li>\n<li>Coordinates failover of dependent providers and community assets<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Enterprise-ready_integration\"><\/span><strong>Enterprise-ready integration<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>Integrates with present <a href=\"https:\/\/en.wikipedia.org\/wiki\/Linux\" target=\"_blank\" rel=\"noopener noreferrer\">Linux<\/a> infrastructure administration<\/li>\n<li>Helps advanced dependency chains and useful resource constraints<\/li>\n<li>Supplies detailed logging and monitoring for compliance necessities<\/li>\n<\/ul>\n<p>Collectively, these instruments preserve your edge workloads operating throughout {hardware} failures or community disruptions.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Structure_overview_Excessive_availability_patterns\"><\/span><strong>Structure overview: Excessive availability patterns<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>AWS IoT Greengrass excessive availability will be applied utilizing two major patterns, every optimized for various use instances.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"EnergeticPassive_configuration_Maximizing_knowledge_consistency\"><\/span><strong>Energetic\/Passive configuration: Maximizing knowledge consistency<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>This mode maximizes knowledge consistency and automatic failover\u2014perfect for mission-critical purposes the place knowledge integrity and repair continuity are paramount. One node runs Greengrass actively whereas the opposite stands prepared in standby mode. A software-based, block-level knowledge replication service like Distributed Replicated Block Gadget (DRBD) ensures on the spot state synchronization between nodes, enabling failover with zero knowledge loss and sustaining gadget identification.<\/p>\n<div class=\"hide-language\">\n<pre><code class=\"lang-markup\">\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510    \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \n\u2502   Major Node  \u2502    \u2502  Standby Node   \u2502 \n\u2502                 \u2502    \u2502                 \u2502 \n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502    \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 \n\u2502 \u2502 Greengrass  \u2502 \u2502    \u2502 \u2502 Greengrass  \u2502 \u2502 \n\u2502 \u2502   ACTIVE    \u2502 \u2502    \u2502 \u2502  STANDBY    \u2502 \u2502 \n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502    \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 \n\u2502                 \u2502    \u2502                 \u2502 \n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502    \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 \n\u2502 \u2502   DRBD      \u2502\u25c4\u253c\u2500\u2500\u2500\u2500\u253c\u25ba\u2502   DRBD      \u2502 \u2502 \n\u2502 \u2502  Major    \u2502 \u2502    \u2502 \u2502 Secondary   \u2502 \u2502 \n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502    \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 \n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518    \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 <\/code><\/pre>\n<\/p><\/div>\n<h4><span class=\"ez-toc-section\" id=\"Key_advantages\"><\/span><strong><em>Key advantages:<\/em><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>This configuration ensures full state preservation throughout failover with sub-minute downtime, zero knowledge loss for in-flight transactions and significant operations, whereas sustaining gadget identification, certificates, and Stream Supervisor persistence seamlessly.<\/p>\n<h4><span class=\"ez-toc-section\" id=\"Actual-world_use_instances\"><\/span><strong><em>Actual-world use instances:<\/em><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>Energetic\/Passive configurations are important in eventualities requiring zero or minimal knowledge loss, similar to in-flight leisure methods that deal with offline cost processing and battery manufacturing services the place manufacturing strains depend upon steady knowledge move from essential manufacturing sensors and ML mannequin outputs to keep up operational integrity and high quality management.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"EnergeticEnergetic_Most_throughput_and_scalability\"><\/span><strong>Energetic\/Energetic: Most throughput and scalability<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>This mode maximizes throughput and offers horizontal scaling for high-volume workloads. A number of unbiased Greengrass situations run concurrently throughout cluster nodes, with clever load balancing distributing work based mostly on node well being and capability. Every node operates with its personal distinctive gadget credentials and configurations.<\/p>\n<div class=\"hide-language\">\n<pre><code class=\"lang-markup\">\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510    \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \n\u2502   Node 1        \u2502    \u2502   Node 2        \u2502 \n\u2502                 \u2502    \u2502                 \u2502 \n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502    \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 \n\u2502 \u2502 Greengrass  \u2502 \u2502    \u2502 \u2502 Greengrass  \u2502 \u2502 \n\u2502 \u2502   ACTIVE    \u2502 \u2502    \u2502 \u2502   ACTIVE    \u2502 \u2502 \n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502    \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 \n\u2502                 \u2502    \u2502                 \u2502 \n\u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502    \u2502 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502 \n\u2502 \u2502Load Balancer\u2502\u25c4\u253c\u2500\u2500\u2500\u2500\u253c\u25ba\u2502Load Balancer\u2502 \u2502 \n\u2502 \u2502 (A\/P Mode)  \u2502 \u2502    \u2502 \u2502 (Standby)   \u2502 \u2502 \n\u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502    \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502 \n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\u00a0\u00a0\u00a0 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\u00a0<\/code><\/pre>\n<\/p><\/div>\n<h4><span class=\"ez-toc-section\" id=\"Key_advantages-2\"><\/span><strong><em>Key advantages:<\/em><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>These configurations allow horizontal scaling for high-throughput eventualities, enhance useful resource utilization throughout nodes, and supply swish degradation underneath partial failures.<\/p>\n<h4><span class=\"ez-toc-section\" id=\"Actual-world_use_instances-2\"><\/span><strong><em>Actual-world use instances:<\/em><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p>Energetic\/Energetic configurations are perfect for high-volume eventualities similar to automotive components manufacturing services and large-scale manufacturing operations with a number of manufacturing strains, the place every node handles completely different line segments to offer each redundancy and elevated processing capability for real-time analytics and anomaly detection.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Configuration_choice_information\"><\/span><strong>Configuration choice information<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Use Energetic\/Passive for purposes that require zero knowledge loss, shared state, and gadget identification preservation. This sample works effectively whenever you want a single level of management and might settle for failover instances underneath one minute.Use Energetic\/Energetic whenever you want excessive throughput and horizontal scaling. This sample fits purposes that may function independently with out shared state, the place load distribution offers operational advantages, and swish degradation is preferable to finish failover.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_one_can_implement_the_answer\"><\/span><strong>How one can implement the answer<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The whole playbook, together with detailed configuration examples and testing procedures, is obtainable within the <a href=\"https:\/\/github.com\/aws-samples\/sample-greengrass-ha-pacemaker\" target=\"_blank\" rel=\"noopener noreferrer\">GitHub respository<\/a>. This offers an Energetic\/Passive implementation automation utilizing <a href=\"https:\/\/github.com\/ansible\/ansible\" target=\"_blank\" rel=\"noopener noreferrer\">Ansible<\/a> that you could customise to your particular necessities. Energetic\/Energetic setup steps are additionally accessible in <a href=\"https:\/\/github.com\/aws-samples\/sample-ha-for-greengrass-with-pacemaker\/tree\/main\/docs\/MANUAL-SETUP-GUIDE.md\" target=\"_blank\" rel=\"noopener noreferrer\">MANUAL-SETUP-GUIDE<\/a> inside the identical repository.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Setup_steps\"><\/span>Setup steps<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><strong>1. Surroundings setup<\/strong><\/p>\n<p>Clone the repository and arrange the event surroundings<\/p>\n<div class=\"hide-language\">\n<pre><code class=\"lang-bash\">git clone https:\/\/github.com\/aws-samples\/sample-greengrass-ha-pacemaker.git\ncd sample-greengrass-ha-pacemaker\n.\/scripts\/setup-dev-env.sh &amp;&amp; supply .venv\/bin\/activate\n<\/code><\/pre>\n<\/p><\/div>\n<p><strong>2. Configure cluster secrets and techniques<\/strong><\/p>\n<p>Generate and encrypt cluster credentials utilizing Ansible Vault<\/p>\n<div class=\"hide-language\">\n<pre><code class=\"lang-bash\"># Create vault password file\necho \"your_secure_password\" &gt; .vault_pass\nchmod 600 .vault_pass\n# Auto-generate encrypted secrets and techniques\n.\/scripts\/setup-vault.sh<\/code><\/pre>\n<\/p><\/div>\n<p>This creates `vars\/cluster-vault.yml` with encrypted credentials for cluster authentication and DRBD replication.<\/p>\n<p><strong>3. Put together Greengrass credentials<\/strong><\/p>\n<p><em>Be aware: This strategy is designed for testing and demonstration functions solely.<\/em><\/p>\n<p>Obtain Greengrass set up recordsdata from AWS IoT Console.<\/p>\n<ol>\n<li>Navigate to AWS IoT Core console \u2192 Greengrass \u2192 Core units<\/li>\n<li>Click on \u2018Arrange one core gadget\u2019 \u2192 \u2018Arrange a tool with installer obtain\u2019<\/li>\n<li>Title your gadget (e.g., \u2018greengrass-ha-device\u2019)<\/li>\n<li>Choose or create a Factor Group<\/li>\n<li>Obtain each recordsdata and rename them:\n<ol>\n<li>Rename hash-setup.sh to greengrass-setup.sh<\/li>\n<li>Rename hash.zip to greengrass-certs.zip<\/li>\n<\/ol>\n<\/li>\n<li>Place recordsdata in `recordsdata\/greengrass\/` listing<\/li>\n<\/ol>\n<p><strong>4. Deploy and configure<\/strong><\/p>\n<p>It will deploy <a href=\"https:\/\/aws.amazon.com\/ec2\/\" target=\"_blank\" rel=\"noopener noreferrer\">AWS EC2<\/a> and mandatory assets to check on AWS.<\/p>\n<div class=\"hide-language\">\n<pre><code class=\"lang-bash\"># Deploy infrastructure\nmake cdk-deploy &amp;&amp; make cdk-inventory\n# Retrieve SSH personal key\n.\/scripts\/get-ssh-key.sh\n# Configure HA cluster\nansible-playbook playbooks\/setup\/system-prerequisites.yml -i stock\/cdk-dev-hosts\nansible-playbook playbooks\/setup\/configure-ha.yml -i stock\/cdk-dev-hosts --vault-password-file .vault_pass<\/code><\/pre>\n<\/p><\/div>\n<p><strong>5. Validate and take a look at<\/strong><\/p>\n<p>Examine cluster standing and optionally, run an automatic failover take a look at.<\/p>\n<div class=\"hide-language\">\n<pre><code class=\"lang-bash\"># Examine cluster standing\nansible node-1 -i stock\/cdk-dev-hosts -m shell -a \"sudo pcs standing\" --become\n# Take a look at failover (non-compulsory)\nansible-playbook playbooks\/testing\/test-failover-simulation.yml -i stock\/cdk-dev-hosts --vault-password-file .vault_pass<\/code><\/pre>\n<\/p><\/div>\n<p>The automated exams validate useful resource migration, DRBD promotion, and knowledge consistency throughout failover.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Cleanup\"><\/span>Cleanup<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>It will destroy the assets created by CDK.<\/p>\n<div class=\"hide-language\">\n<pre><code class=\"lang-bash\"># Destroy infrastructure\nmake cdk-destroy<\/code><\/pre>\n<\/p><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion_Enterprise-ready_edge_computing\"><\/span><strong>Conclusion: Enterprise-ready edge computing<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>AWS IoT Greengrass and Pacemaker collectively present the excessive availability wanted for mission-critical edge deployments. Through the use of Pacemaker\u2019s cluster administration capabilities, organizations can confidently deploy Greengrass the place reliability is crucial.Whether or not you\u2019re managing industrial management methods, processing real-time analytics, or orchestrating edge AI workloads, this architectural sample offers the inspiration for resilient, scalable edge computing that your online business can depend upon.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Subsequent_steps\"><\/span>Subsequent steps<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Able to implement enterprise-grade excessive availability to your AWS IoT Greengrass deployments? Right here\u2019s your path ahead:<\/p>\n<p>Repository: <a href=\"https:\/\/github.com\/aws-samples\/sample-greengrass-ha-pacemaker\" target=\"_blank\" rel=\"noopener noreferrer\">sample-greengrass-ha-pacemaker<\/a><\/p>\n<hr\/>\n<h3><span class=\"ez-toc-section\" id=\"In_regards_to_the_authors\"><\/span>In regards to the authors<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p style=\"clear: both\"><img decoding=\"async\" class=\"alignleft wp-image-4649 size-full\" src=\"https:\/\/d2908q01vomqb2.cloudfront.net\/f6e1126cedebf23e1463aee73f9df08783640400\/2025\/11\/15\/yongjiff-headshot.png\" alt=\"\"><strong>Yong Ji <\/strong>Yong Ji is a Senior Options Architect at Amazon Net Companies (AWS), serving to enterprises construct modern cloud-based options. With over 25 years of expertise in cloud structure, analytics and knowledge engineering, Yong brings deep technical experience and a ardour for fixing advanced enterprise challenges. Exterior of labor, Yong is a passionate desk tennis participant.<\/p>\n<p style=\"clear: both\"><img decoding=\"async\" class=\"size-full wp-image-4648 alignleft\" src=\"https:\/\/d2908q01vomqb2.cloudfront.net\/f6e1126cedebf23e1463aee73f9df08783640400\/2025\/11\/15\/sidsriv-headshot.png\" alt=\"\"><strong>Siddhant Srivastava <\/strong>Siddhant Srivastava is a Software program Improvement Engineer with AWS IoT Greengrass. He has 3+ years of expertise in edge computing with concentrate on constructing resilient, scalable distributed methods. Exterior work, Siddhant participates in soccer leagues and billiards tournaments.<\/p>\n<p>       <!-- '\"` -->\n      <\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Edge computing downtime in industrial IoT environments will be each inconvenient and dear. Methods on the edge require steady operation to keep up enterprise continuity. Whereas AWS IoT Greengrass delivers highly effective edge computing capabilities, attaining true enterprise-grade excessive availability requires further orchestration. This put up exhibits the way to use Pacemaker, a cluster useful [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":17470,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22],"tags":[],"class_list":{"0":"post-17468","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-iot"},"_links":{"self":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts\/17468","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=17468"}],"version-history":[{"count":1,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts\/17468\/revisions"}],"predecessor-version":[{"id":17469,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/posts\/17468\/revisions\/17469"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=\/wp\/v2\/media\/17470"}],"wp:attachment":[{"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17468"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17468"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aireviewirush.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17468"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}