SaltStack Config and Ansible Tower are two powerful configuration management and infrastructure automation tools that cater to the needs of DevOps teams across the globe. While SaltStack Config is an open-source solution, Ansible Tower is the commercial, enterprise-ready version of Ansible Open Source. In this blog post, we will compare SaltStack Config and Ansible Tower in terms of architecture, ease of use, scalability, and features to help you make an informed decision on which tool is best suited for your requirements.

1. Architecture:

SaltStack Config: SaltStack Config employs a master-minion architecture, where a central master server controls multiple minion nodes. This structure enables powerful parallel processing, as the master server can send commands to all connected minions simultaneously. SaltStack uses a ZeroMQ-based messaging protocol for communication between the master and minions, ensuring better performance and lower latency compared to SSH-based solutions.

Ansible Tower: Ansible Tower is built on top of the open-source Ansible project and retains its agentless architecture, where all operations are executed on target nodes via SSH (or WinRM for Windows hosts). However, Ansible Tower adds a powerful web-based user interface, role-based access control, and centralized management capabilities to the core Ansible features.

2. Ease of Use:

SaltStack Config: SaltStack Config utilizes YAML-based configuration files called “states” to define the desired configuration of a system. The tool uses Jinja2 templating, allowing for dynamic configuration generation and flexibility in managing complex environments. SaltStack Config also offers a secure data management system called “Pillar” for storing and handling sensitive data.

Ansible Tower: Ansible Tower provides a user-friendly web interface, making it easier for teams to manage their infrastructure without requiring deep knowledge of the underlying Ansible Open Source technology. Like SaltStack Config, Ansible Tower also uses YAML-based configuration files (playbooks) and supports Jinja2 templating.

3. Scalability:

SaltStack Config: The master-minion architecture of SaltStack Config allows it to handle thousands of nodes efficiently, making it a popular choice for large-scale deployments. While a single master server can become a bottleneck in very large environments, this issue can be mitigated using techniques like multi-master setups or syndics.

Ansible Tower: Ansible Tower enhances the scalability of Ansible Open Source through features like clustering, which allows multiple Tower instances to work together to manage large-scale infrastructures. While the underlying agentless architecture still presents some scalability challenges, Ansible Tower addresses them to a significant extent with enterprise-grade features.

4. Features:

SaltStack Config: SaltStack Config offers powerful features like parallel execution, event-driven automation, and remote execution, making it a versatile and efficient choice for configuration management and infrastructure automation. Additionally, the tool provides extensive support for cloud platforms, container management, and network automation.

Ansible Tower: Ansible Tower builds upon the core features of Ansible Open Source and adds enterprise-ready capabilities like a web-based user interface, role-based access control, job scheduling, and centralized logging and auditing. The tool also provides integration with popular third-party services and supports a wide range of plugins and modules.

Conclusion:

Both SaltStack Config and Ansible Tower are powerful and feature-rich configuration management and infrastructure automation tools. SaltStack Config stands out with its master-minion architecture and superior scalability, making it well-suited for large-scale deployments. On the other hand, Ansible Tower offers a user-friendly web interface and enterprise-grade features, catering to organizations that require a more streamlined and centralized solution. The choice between the two tools depends on your specific requirements, infrastructure size, and the level of complexity you need to manage. Evaluating both tools within the context

SaltStack Config and Ansible Open Source are two popular configuration management and infrastructure automation tools used by DevOps teams across the globe. Both solutions have their own unique set of features, advantages, and drawbacks. In this blog post, we will compare SaltStack Config (formerly known as Salt) and Ansible Open Source in terms of their architecture, ease of use, scalability, and community support, to help you make an informed decision on which tool is best suited for your needs.

1. Architecture:

SaltStack Config: SaltStack Config is built on a master-minion architecture, where a central master server controls multiple minion nodes. This structure enables powerful parallel processing, as the master server can send commands to all connected minions simultaneously. SaltStack uses a ZeroMQ-based messaging protocol for communication between the master and minions.

Ansible Open Source: Ansible, on the other hand, relies on an agentless architecture, where all operations are executed on the target nodes via SSH (or WinRM for Windows hosts). This approach simplifies deployment and reduces overhead, as there is no need to install any software on the target nodes.

2. Ease of Use:

SaltStack Config: SaltStack Config utilizes YAML-based configuration files called “states” to define the desired configuration of a system. The tool uses Jinja2 templating, which allows for dynamic configuration generation. Additionally, SaltStack Config offers a feature called “Pillar” for securely managing sensitive data.

Ansible Open Source: Ansible also uses YAML-based configuration files called “playbooks” to define the desired state of a system. The tool supports Jinja2 templating as well and has a built-in mechanism for managing sensitive data called “Ansible Vault.” The learning curve for Ansible is generally considered to be lower than that of SaltStack Config, mainly because of its agentless architecture and more straightforward syntax.

3. Scalability:

SaltStack Config: Due to its master-minion architecture, SaltStack Config can handle thousands of nodes efficiently. The parallel execution of tasks significantly reduces the time required for configuration management and orchestration. However, a single master server can become a bottleneck in very large-scale deployments.

Ansible Open Source: Ansible’s agentless architecture can make it less scalable than SaltStack Config in large environments. The performance of Ansible largely depends on the resources available on the control node, as it must establish and maintain SSH connections with each target host. Nevertheless, it is possible to mitigate scalability issues by using tools like Ansible Tower or by employing techniques such as parallelism and batching.

4. Community Support:

SaltStack Config: SaltStack Config has a robust and active community that regularly contributes to its development. However, since the acquisition of SaltStack by VMware in 2020, the future of the open-source edition is uncertain, and the community may become more fragmented.

Ansible Open Source: Ansible has a large and active community of users and contributors, and it is backed by Red Hat, which was acquired by IBM in 2019. The tool has continued to grow in popularity, and the open-source edition enjoys regular updates and a rich ecosystem of third-party modules and plugins.

Conclusion:

Both SaltStack Config and Ansible Open Source are powerful configuration management and infrastructure automation tools, each with its own strengths and weaknesses. The choice between the two largely depends on your specific requirements, infrastructure size, and familiarity with the tools. While SaltStack Config offers better scalability and parallel execution, Ansible Open Source provides a more straightforward learning curve and agentless architecture. Ultimately, you should evaluate both tools within the context of your environment to determine the best fit.

Migrating virtual machines (VMs) from a cloud environment to an on-premises VMware vSphere infrastructure can be a daunting task. However, with the right tools and processes in place, it can be a seamless and efficient process. One such tool is the VMware Converter, which enables users to convert native cloud VMs\physical servers to vSphere machines. In this blog post, we will discuss the benefits and challenges of converting cloud VMs and provide a step-by-step guide for using VMware Converter to achieve this goal.

Benefits of Converting Cloud VMs to vSphere Machines

1. Cost Savings: Moving VMs from the cloud to on-premises can result in significant cost savings, especially for organizations with large-scale cloud deployments. On-prem infrastructure typically incurs lower ongoing costs compared to cloud-based services.
2. Data Security and Compliance: By hosting VMs on your own infrastructure, you can better control data security and ensure compliance with regulatory requirements. This is particularly important for organizations operating in highly regulated industries.
3. Enhanced Performance: On-premises hardware can be tailored to meet specific performance needs, potentially providing better performance than cloud-based VMs.

Challenges of Converting Cloud VMs to On-Prem vSphere Machines

1. Compatibility: Different cloud providers and hypervisors use different virtual machine formats, which can pose compatibility issues during the conversion process. VMware Converter simplifies this process by providing a unified conversion tool.
2. Downtime: Converting VMs may require temporary downtime, which can impact business operations. Proper planning and scheduling can help minimize downtime and disruption.

Step-by-Step Guide to Convert Native Cloud VMs to On-Prem vSphere with VMware Converter

Step 1: Prepare Your Environment Before you start the conversion process, make sure your on-prem vSphere environment is set up and ready to host the converted VMs. This includes ensuring adequate storage, compute resources, and network connectivity.

Step 2: Download and Install VMware Converter Download the latest version of VMware Converter from the VMware website and install it on a Windows-based system that has network access to both the cloud VMs and your on-prem vSphere environment. The download page can be found here. The documentation can be found here. Take a note of the ports as they will need to be open on the firewalls. Ex for the cloud VM we need incoming TCP ports 445, 139, 9089, 9090, and UDP ports 137 and 138.

Step 3 (optional): In order to be able to revert in case of a failure its highly recommended that we take a backup. This can be achieved by creating a snapshot or image of the VM. Consult your cloud provider’s documentation for the exact steps to create a snapshot or image.

Step 4: Run the Conversion Process Open VMware Converter and select “Convert Machine” from the main menu. Choose “Powered-off source” and “Virtual Appliance” as the source type. Browse to the captured VM image file and select it as the source. Next, select your on-prem vSphere environment as the destination and provide the required credentials.

Step 5: Configure the Destination VM In the VMware Converter wizard, configure the destination VM’s settings such as datastore, network, and virtual hardware according to your on-prem environment. You may also need to resize the VM’s virtual disks or adjust its memory and CPU resources.

Step 6: Start the Conversion Click “Finish” to start the conversion process. Monitor the progress in the VMware Converter interface. The time it takes to complete the conversion depends on the size of the VM and network bandwidth.

Step 7: Power On and Test the Converted VM Once the conversion process is complete, power on the converted VM in your on-prem vSphere environment and test it to ensure it is functioning correctly. Make any necessary adjustments and retest as needed.

Converting native cloud VMs to on-prem vSphere machines using VMware Converter can offer several benefits, including cost savings, enhanced data security, and potentially better performance. By following the step-by-step guide outlined above, you can streamline the migration process and ensure a smooth transition from the cloud to your on-prem infrastructure. Remember to properly plan and schedule your migration to minimize downtime and business disruption. With VMware Converter, you can leverage the advantages of both cloud and on-prem environments while maintaining control and flexibility over your IT infrastructure.

Introduction

When it comes to deploying a three-tier application in Google Cloud Platform (GCP), there are several tools available, including vRealize Automation (vRA) 8, Terraform, and Ansible. Each tool has its own strengths and weaknesses, and choosing the right one for your project depends on several factors. In this blog post, we will compare these three tools and discuss how vRA 8 stands out as the best option for deploying a three-tier application in GCP.

Overview of vRA 8, Terraform, and Ansible

vRealize Automation (vRA) 8 is an enterprise-grade cloud automation and management platform that allows you to automate the deployment and management of complex applications and infrastructure. It provides a wide range of tools and services that can be used to deploy and run applications in the cloud, including GCP.

Terraform is an open-source infrastructure as code (IaC) tool that allows you to define, deploy, and manage infrastructure in a consistent and repeatable way. It uses a simple, declarative language for defining infrastructure and supports many cloud providers, including GCP.

Ansible is an open-source automation tool that allows you to automate configuration management, application deployment, and task automation. It uses a simple, human-readable YAML syntax for defining tasks and supports many cloud providers, including GCP.

Comparison of vRA 8, Terraform, and Ansible
When it comes to deploying a three-tier application in GCP, each tool has its own strengths and weaknesses. Let’s take a look at how vRA 8, Terraform, and Ansible compare.

Ease of Use

vRA 8 is an enterprise-grade platform that provides a user-friendly interface for deploying and managing infrastructure and applications. It has a drag-and-drop interface for creating blueprints, which makes it easy to create and manage complex applications. It also provides a centralized platform for managing infrastructure, which can be useful for large organizations with many teams.

Terraform is a powerful IaC tool that requires some knowledge of infrastructure and coding. It uses a declarative language for defining infrastructure, which can take some time to learn. However, once you understand the syntax, it can be very powerful and flexible.

Ansible is a simple and easy-to-use automation tool that uses a human-readable YAML syntax for defining tasks. It does not require any coding knowledge and can be learned quickly by IT operations teams.

Scalability

vRA 8 is designed to handle large-scale deployments and provides many tools for managing infrastructure at scale. It can handle complex application deployments and can scale to meet the needs of large organizations.

Terraform is also designed to handle large-scale deployments and provides many tools for managing infrastructure at scale. It can handle complex application deployments and can scale to meet the needs of large organizations.

Ansible is not designed for large-scale deployments and can be difficult to scale for large organizations. However, it is a good option for small to medium-sized organizations that need to automate simple tasks.

Flexibility

vRA 8 is a very flexible platform that provides many tools and services for deploying and managing infrastructure and applications. It can integrate with many other tools and services, which makes it a good option for complex environments.

Terraform is also a very flexible tool that provides many options for defining infrastructure. It supports many cloud providers and can be used to deploy complex applications.

Ansible is a flexible tool that can be used for many different tasks, including configuration management, application deployment, and task automation. It supports many cloud providers and can be used to automate many different tasks.

Cost

vRA 8 is an enterprise-grade platform that requires a license and can be expensive for small organizations.

Terraform is an open-source tool that is free to use

Ansible is also an open-source tool that is free to use.

Why vRA 8 stands out for deploying a three-tier application in GCP While Terraform and Ansible are both great tools for deploying infrastructure, vRA 8 stands out for deploying a three-tier application in GCP for several reasons.

Firstly, vRA 8 is a powerful platform that provides a user-friendly interface for creating blueprints and managing infrastructure. It is designed to handle large-scale deployments and provides many tools for managing infrastructure at scale.

Secondly, vRA 8 provides many integration options with other tools and services, which makes it a good option for complex environments. It can integrate with many different cloud providers, including GCP, and can be used to automate complex application deployments.

Finally, vRA 8 provides many advanced features, such as self-service provisioning, policy-based governance, and cloud cost management, which makes it a good option for enterprise-grade applications.

Conclusion

When it comes to deploying a three-tier application in GCP, vRealize Automation (vRA) 8, Terraform, and Ansible are all good options. Each tool has its own strengths and weaknesses, and the best choice for your project depends on several factors. While Terraform and Ansible are both great tools for deploying infrastructure, vRA 8 stands out as the best option for deploying a three-tier application in GCP due to its powerful platform, user-friendly interface, and advanced features.

Ansible is an open-source automation tool that allows you to automate configuration management, application deployment, and task automation. In this blog post, we will explore how to deploy a three-tier application in Google Cloud Platform (GCP) using Ansible.

GCP Overview Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google. It provides a wide range of services such as virtual machines, storage, and networking, among others, that can be used to deploy and run applications in the cloud. One of the advantages of using GCP is its high availability and scalability, which makes it an excellent option for deploying enterprise-grade applications.

Ansible Overview Ansible is an open-source automation tool that enables you to automate configuration management, application deployment, and task automation. It uses a simple, human-readable YAML syntax for defining tasks and supports many cloud providers, including GCP.

Deploying a Three-Tier Application using Ansible in GCP To deploy a three-tier application using Ansible in GCP, we will use an Ansible playbook. A playbook is a series of tasks that are executed on a set of hosts defined in an inventory file. In this example, we will use an Ansible playbook to deploy a simple three-tier application that consists of a web server, application server, and database server.

The following are the steps required to deploy the application:

1. Set up a GCP project and enable the Compute Engine API.
2. Create a service account in GCP and assign it the necessary roles.
3. Install Ansible and the GCP Ansible modules.
4. Write an Ansible playbook that defines the components and dependencies of the application.
5. Run the Ansible playbook to create the infrastructure.
6. Monitor the infrastructure in GCP.

Let’s go through each step in detail.

Step 1: Set up a GCP project and enable the Compute Engine API. To set up a GCP project and enable the Compute Engine API, follow these steps:

1. Go to the GCP Console (console.cloud.google.com).
2. Click on the project drop-down menu in the top left corner of the screen.
3. Click on the “New Project” button.
4. Enter a name for the project and click on the “Create” button.
5. Once the project is created, select it from the project drop-down menu.
6. Enable the Compute Engine API by navigating to APIs & Services > Library and searching for Compute Engine. Click on the “Enable” button.

Step 2: Create a service account in GCP and assign it the necessary roles. To create a service account in GCP, follow these steps:

1. Go to the GCP Console (console.cloud.google.com).
2. Navigate to the IAM & Admin > Service Accounts page.
3. Click on the “Create Service Account” button.
4. Enter a name for the service account and click on the “Create” button.
5. On the “Create Key” tab, select “JSON” as the key type and click on the “Create” button. This will download a JSON file containing the service account key.
6. Assign the service account the necessary roles by navigating to IAM & Admin > IAM and adding the roles “Compute Instance Admin (v1)” and “Service Account User” to the service account.

Step 3: Install Ansible and the GCP Ansible modules. To install Ansible and the GCP Ansible modules, follow these steps:

1. Install Ansible on your local machine using the appropriate method for your operating system.
2. Install the Google Cloud SDK by following the instructions on the Google Cloud SDK documentation page.
3. Install the GCP Ansible modules by running the command “pip install requests google -auth google-auth google-auth-httplib2 google-api-python-client google-cloud-ansible” in a terminal window.

Step 4: Write an Ansible playbook that defines the components and dependencies of the application. To write an Ansible playbook that defines the components and dependencies of the application, follow these steps:

1. Create a new directory for the Ansible playbook.
2. Create an inventory file that lists the GCP instances that will be created.
3. Create a playbook YAML file that defines the tasks required to create the infrastructure for the three-tier application.
4. Define any necessary variables for the playbook.
5. Use the GCP Ansible modules to manage the GCP resources.

Here’s an example Ansible playbook that deploys a three-tier application:

- hosts: all
  gather_facts: no
  tasks:
  - name: create network
    gcp_compute_network:
      name: my-network
      auto_create_subnetworks: false
      auth_kind: serviceaccount
      service_account_file: "{{ lookup('env','GOOGLE_APPLICATION_CREDENTIALS') }}"

  - name: create subnet
    gcp_compute_subnetwork:
      name: my-subnetwork
      network: my-network
      region: us-central1
      ip_cidr_range: "10.0.0.0/24"
      auth_kind: serviceaccount
      service_account_file: "{{ lookup('env','GOOGLE_APPLICATION_CREDENTIALS') }}"

  - name: create web server
    gcp_compute_instance:
      name: web-server
      machine_type: f1-micro
      zone: us-central1-a
      disks:
      - auto_delete: true
        boot: true
        initialize_params:
          source_image: projects/centos-cloud/global/images/family/centos-7
      network_interfaces:
      - network: my-network
        subnetwork: my-subnetwork
      metadata:
        startup-script: "#!/bin/bash\nyum install -y httpd\nsystemctl enable httpd\nsystemctl start httpd\n"
      auth_kind: serviceaccount
      service_account_file: "{{ lookup('env','GOOGLE_APPLICATION_CREDENTIALS') }}"

  - name: create app server
    gcp_compute_instance:
      name: app-server
      machine_type: n1-standard-1
      zone: us-central1-a
      disks:
      - auto_delete: true
        boot: true
        initialize_params:
          source_image: projects/centos-cloud/global/images/family/centos-7
      network_interfaces:
      - network: my-network
        subnetwork: my-subnetwork
      metadata:
        startup-script: "#!/bin/bash\nyum install -y java-1.8.0-openjdk\ncurl -L https://tomcat.apache.org/download-80.cgi#8.5.38 -o /tmp/tomcat.tar.gz\nmkdir /opt/tomcat\ntar xzvf /tmp/tomcat.tar.gz -C /opt/tomcat --strip-components=1\nsystemctl enable tomcat\nsystemctl start tomcat\n"
      auth_kind: serviceaccount
      service_account_file: "{{ lookup('env','GOOGLE_APPLICATION_CREDENTIALS') }}"

  - name: create database server
    gcp_compute_instance:
      name: database-server
      machine_type: n1-standard-2
      zone: us-central1-a
      disks:
      - auto_delete: true
        boot: true
        initialize_params:
          source_image: projects/centos-cloud/global/images/family/centos-7
      network_interfaces:
      - network: my-network
        subnetwork: my-subnetwork
      metadata:
        startup-script: "#!/bin/bash\nyum install -y mariadb-server\nsystemctl enable mariadb\nsystemctl start mariadb\n"
      auth_kind: serviceaccount
      service_account_file: "{{ lookup('env','GOOGLE_APPLICATION_CREDENTIALS') }}"

  - name: wait for web server
    wait_for:
      host: "{{ hostvars['web-server']['networkInterfaces'][0]['accessConfigs'][0]['natIP'] }}"
      port: 80
      delay: 10
      timeout: 120

  - name: wait for app server
    wait_for:
      host: "{{ hostvars['app-server']['networkInterfaces'][0]['accessConfigs'][0]['natIP'] }}"
      port: 8080
      delay: 10
      timeout: 120

Step 5: Run the Ansible playbook to create the infrastructure. To run the Ansible playbook to create the infrastructure, follow these steps:

1. Open a terminal window and navigate to the directory where you saved the Ansible playbook.
2. Run the command “export GOOGLE_APPLICATION_CREDENTIALS=path/to/credentials.json” to set the path to the service account key file.
3. Run the command “ansible-playbook playbook.yml” to run the playbook and create the infrastructure.

Step 6: Monitor the infrastructure in GCP. To monitor the infrastructure in GCP, follow these steps:

1. In GCP, navigate to the Compute Engine > Instances page to view the deployed VM instances.
2. Monitor the VM instance status and any associated logs or metrics.

Conclusion Deploying a three-tier application using Ansible in GCP can be a powerful and flexible way to provision infrastructure. By using Ansible, you can automate the deployment of infrastructure and manage it as code. GCP provides many services that can be used to deploy and run applications in the cloud, and by combining Ansible and GCP, you can create a robust and scalable application infrastructure.

Deploying a three-tier application using Terraform is a popular approach because it provides infrastructure-as-code benefits. Terraform is an open-source infrastructure-as-code tool that allows you to define, configure, and manage infrastructure in a declarative language. In this blog post, we will explore how to deploy a three-tier application in Google Cloud Platform (GCP) using Terraform.

GCP Overview

Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google. It provides a wide range of services such as virtual machines, storage, and networking, among others, that can be used to deploy and run applications in the cloud. One of the advantages of using GCP is its high availability and scalability, which makes it an excellent option for deploying enterprise-grade applications.

Terraform Overview

Terraform is an open-source infrastructure-as-code tool that enables you to define, configure, and manage infrastructure in a declarative language. It supports many cloud providers, including GCP, and enables you to automate infrastructure provisioning, configuration, and management.

Deploying a Three-Tier Application using Terraform in GCP

To deploy a three-tier application using Terraform in GCP, we will use a module. A module is a self-contained Terraform configuration that encapsulates a set of resources and their dependencies. In this example, we will use a module to deploy a simple three-tier application that consists of a web server, application server, and database server.

The following are the steps required to deploy the application:

1. Set up a GCP project and enable the Compute Engine API.
2. Create a service account in GCP and assign it the necessary roles.
3. Write a Terraform module that defines the components and dependencies of the application.
4. Initialize the Terraform module and run the Terraform plan command.
5. Apply the Terraform configuration to create the infrastructure.
6. Monitor the infrastructure in GCP.

Let’s go through each step in detail.

Step 1: Set up a GCP project and enable the Compute Engine API. To set up a GCP project and enable the Compute Engine API, follow these steps:

1. Go to the GCP Console (console.cloud.google.com).
2. Click on the project drop-down menu in the top left corner of the screen.
3. Click on the “New Project” button.
4. Enter a name for the project and click on the “Create” button.
5. Once the project is created, select it from the project drop-down menu.
6. Enable the Compute Engine API by navigating to APIs & Services > Library and searching for Compute Engine. Click on the “Enable” button.

Step 2: Create a service account in GCP and assign it the necessary roles. To create a service account in GCP, follow these steps:

1. Go to the GCP Console (console.cloud.google.com).
2. Navigate to the IAM & Admin > Service Accounts page.
3. Click on the “Create Service Account” button.
4. Enter a name for the service account and click on the “Create” button.
5. On the “Create Key” tab, select “JSON” as the key type and click on the “Create” button. This will download a JSON file containing the service account key.
6. Assign the service account the necessary roles by navigating to IAM & Admin > IAM and adding the roles “Compute Instance Admin (v1)” and “Service Account User” to the service account.

Step 3: Write a Terraform module that defines the components and dependencies of the application. To write a Terraform module that defines the components and dependencies of the application, follow these steps:

1. Create a new directory for the Terraform module.
2. Create a main.tf file in the directory and define the necessary resources for the three-tier application, such as Compute Engine instances, disks, and networking components.
3. Define any necessary dependencies between the resources, such as making the application server depend on the database server.
4. Define any necessary variables and outputs for the module.
5. Use the Google Cloud Platform provider in Terraform to manage the GCP resources.

Here’s an example Terraform module that deploys a three-tier application:

provider "google" {
  credentials = file("path/to/credentials.json")
  project     = var.project_id
  region      = var.region
}

resource "google_compute_network" "my_network" {
  name                    = "my-network"
  auto_create_subnetworks = false
}

resource "google_compute_subnetwork" "my_subnetwork" {
  name          = "my-subnetwork"
  ip_cidr_range = "10.0.0.0/24"
  network       = google_compute_network.my_network.self_link
  region        = var.region
}

resource "google_compute_instance" "web_server" {
  name         = "web-server"
  machine_type = "f1-micro"
  zone         = var.zone

  boot_disk {
    initialize_params {
      image = "centos-7"
    }
  }

  network_interface {
    network = google_compute_network.my_network.self_link
  }

  metadata_startup_script = <<-EOF
    #!/bin/bash
    yum install -y httpd
    systemctl enable httpd
    systemctl start httpd
  EOF
}

resource "google_compute_instance" "app_server" {
  name         = "app-server"
  machine_type = "n1-standard-1"
  zone         = var.zone

  boot_disk {
    initialize_params {
      image = "centos-7"
    }
  }

  network_interface {
    network = google_compute_network.my_network.self_link
  }

  metadata_startup_script = <<-EOF
    #!/bin/bash
    yum install -y java-1.8.0-openjdk
    curl -L https://tomcat.apache.org/download-80.cgi#8.5.38 -o /tmp/tomcat.tar.gz
    mkdir /opt/tomcat
    tar xzvf /tmp/tomcat.tar.gz -C /opt/tomcat --strip-components=1
    systemctl enable tomcat
    systemctl start tomcat
  EOF
}

resource "google_compute_instance" "database_server" {
  name         = "database-server"
  machine_type = "n1-standard-2"
  zone         = var.zone

  boot_disk {
    initialize_params {
      image = "centos-7"
    }
  }

  network_interface {
    network = google_compute_network.my_network.self_link
  }

  metadata_startup_script = <<-EOF
    #!/bin/bash
    yum install -y mariadb-server
    systemctl enable mariadb
    systemctl start mariadb
  EOF
}

Step 4: Initialize the Terraform module and run the Terraform plan command. To initialize the Terraform module and run the Terraform plan command, follow these steps:

1. Open a terminal window and navigate to the directory where you saved the Terraform module.
2. Run the command “terraform init” to initialize the module and download the necessary provider plugins.
3. Define any necessary variables in a “variables.tf” file.
4. Run the command “terraform plan” to generate a plan of the changes that will be made to the infrastructure.

Step 5: Apply the Terraform configuration to create the infrastructure. To apply the Terraform configuration to create the infrastructure, follow these steps:

1. Run the command “terraform apply” to create the infrastructure.
2. Review the plan that Terraform generates to ensure that the changes are correct.
3. Type “yes” when prompted to confirm the changes.

Step 6: Monitor the infrastructure in GCP. To monitor the infrastructure in GCP, follow these steps:

1. In GCP, navigate to the Compute Engine > Instances page to view the deployed VM instances.
2. Monitor the VM instance status and any associated logs or metrics.

Conclusion Deploying a three-tier application using Terraform in GCP can be a powerful and flexible way to provision infrastructure. By using Terraform, you can automate the deployment of infrastructure and manage it as code. GCP provides many services that can be used to deploy and run applications in the cloud, and by combining Terraform and GCP, you can create a robust and scalable application infrastructure.

Deploying a three-tier application is a common task for many organizations, and as more companies move to the cloud, it’s essential to understand how to deploy such applications in the cloud environment. In this blog post, we will explore how to deploy a three-tier application from vRealize Automation 8 in Google Cloud Platform (GCP) using a blueprint.

GCP Overview

Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google. It provides a wide range of services such as virtual machines, storage, and networking, among others, that can be used to deploy and run applications in the cloud. One of the advantages of using GCP is its high availability and scalability, which makes it an excellent option for deploying enterprise-grade applications.

vRealize Automation 8 Overview

vRealize Automation 8 is a cloud automation platform that enables IT teams to automate the delivery and management of infrastructure, applications, and custom services. It provides a self-service catalog for end-users to request IT services, including the ability to deploy and manage multi-tier applications.

Deploying a Three-Tier Application from vRA 8 in GCP

To deploy a three-tier application from vRA 8 in GCP, we will use a blueprint. A blueprint is a set of instructions that define the components, configuration, and dependencies of an application. In this example, we will use a blueprint to deploy a simple three-tier application that consists of a web server, application server, and database server.

The following are the steps required to deploy the application:

1. Create a new project in GCP and enable the Compute Engine API.
2. Create a service account in GCP and assign it the necessary roles.
3. Create a blueprint in vRA 8 and add the necessary components and dependencies.
4. Publish the blueprint in vRA 8 and create a deployment.
5. Monitor the deployment in vRA 8 and GCP.

Let’s go through each step in detail.

Step 1: Create a new project in GCP and enable the Compute Engine API. To create a new project in GCP, follow these steps:

1. Go to the GCP Console (console.cloud.google.com).
2. Click on the project drop-down menu in the top left corner of the screen.
3. Click on the “New Project” button.
4. Enter a name for the project and click on the “Create” button.
5. Once the project is created, select it from the project drop-down menu.
6. Enable the Compute Engine API by navigating to APIs & Services > Library and searching for Compute Engine. Click on the “Enable” button.

Step 2: Create a service account in GCP and assign it the necessary roles. To create a service account in GCP, follow these steps:

1. Go to the GCP Console (console.cloud.google.com).
2. Navigate to the IAM & Admin > Service Accounts page.
3. Click on the “Create Service Account” button.
4. Enter a name for the service account and click on the “Create” button.
5. On the “Create Key” tab, select “JSON” as the key type and click on the “Create” button. This will download a JSON file containing the service account key.
6. Assign the service account the necessary roles by navigating to IAM & Admin > IAM and adding the roles “Compute Instance Admin (v1)” and “Service Account User” to the service account.

Step 3: Create a blueprint in vRA 8 and add the necessary components and dependencies. To create a blueprint in vRA 8, follow these steps:

1. Log in to the vRA 8 Console.
2. Click on “Design” in the top menu
3. Click on “Blueprints” in the left-hand menu and then click on the “New Blueprint” button.
4. Enter a name for the blueprint and select the “Cloud Template” as the blueprint type.
5. In the blueprint canvas, drag and drop the following components from the component palette onto the canvas: Compute, Load Balancer, Database, and Networking.
6. Connect the components together by dragging and dropping the appropriate connectors between them.
7. Configure the components by double-clicking on them and entering the necessary information such as the VM template, disk size, network settings, etc.
8. Add any necessary dependencies between the components, such as making the application server depend on the database server.
9. Save the blueprint.

Step 4: Publish the blueprint in vRA 8 and create a deployment. To publish the blueprint in vRA 8 and create a deployment, follow these steps:

1. Click on “Publish” in the top menu of the blueprint canvas.
2. Enter a version number and any release notes, and then click on the “Publish” button.
3. Click on “Deployments” in the left-hand menu and then click on the “New Deployment” button.
4. Select the published blueprint from the dropdown list and enter a name for the deployment.
5. Configure any necessary settings such as the number of instances for each component.
6. Click on the “Deploy” button.

Step 5: Monitor the deployment in vRA 8 and GCP. To monitor the deployment in vRA 8 and GCP, follow these steps:

1. In vRA 8, navigate to the deployment’s details page by clicking on the deployment name in the deployments list.
2. Monitor the deployment status and any associated tasks or events.
3. In GCP, navigate to the Compute Engine > Instances page to view the deployed VM instances.
4. Monitor the VM instance status and any associated logs or metrics.

Code Example The following is an example code snippet that can be used to define the components in the blueprint:

resources:
  - name: web-server
    type: Cloud.Machine
    properties:
      flavor: small
      image: CentOS 7
      networks:
        - network: my-network
          assignment: static
          ipAddress: 10.0.0.10
      userData:
        value: |
          #!/bin/bash
          yum install -y httpd
          systemctl enable httpd
          systemctl start httpd

  - name: app-server
    type: Cloud.Machine
    properties:
      flavor: medium
      image: CentOS 7
      networks:
        - network: my-network
          assignment: static
          ipAddress: 10.0.0.20
      userData:
        value: |
          #!/bin/bash
          yum install -y java-1.8.0-openjdk
          curl -L https://tomcat.apache.org/download-80.cgi#8.5.38 -o /tmp/tomcat.tar.gz
          mkdir /opt/tomcat
          tar xzvf /tmp/tomcat.tar.gz -C /opt/tomcat --strip-components=1
          systemctl enable tomcat
          systemctl start tomcat

  - name: database-server
    type: Cloud.Machine
    properties:
      flavor: large
      image: CentOS 7
      networks:
        - network: my-network
          assignment: static
          ipAddress: 10.0.0.30
      userData:
        value: |
          #!/bin/bash
          yum install -y mariadb-server
          systemctl enable mariadb
          systemctl start mariadb

In conclusion, deploying a three-tier application from vRA 8 in GCP using a blueprint can be a straightforward process if you follow the necessary steps. By using GCP, you can benefit from its high availability and scalability features, which are essential for enterprise-grade applications. Additionally, vRA 8’s automation capabilities can help streamline the deployment process and reduce the likelihood of errors. By leveraging these tools and technologies, you can deploy a robust and scalable application infrastructure in the cloud.