OpenStack is an open-source cloud computing platform that provides a set of software tools and components for building and managing public and private clouds. It enables organizations to create and manage cloud infrastructure services, including compute, storage, networking, and more. OpenStack is designed to be highly flexible, scalable, and customizable, making it a popular choice for building cloud solutions.
OpenStack is an open-source cloud computing platform that was initially launched in July 2010 as a joint project by Rackspace Hosting and NASA. Since then, it has grown into a vibrant open-source community with contributions from a wide range of organizations and individuals. Here's a brief history of OpenStack and an overview of its main components:
OpenStack History:
Launch (2010): OpenStack was publicly launched in July 2010 with the release of the first two core projects, Nova (compute) and Swift (object storage). It was created to address the need for an open and flexible cloud computing platform.
Expanding Community (2011-2012): The OpenStack community quickly expanded, with numerous companies joining the project. The community released new versions of OpenStack, including Diablo, Essex, and Folsom, each with additional core and supporting projects.
Foundation Establishment (2012): In September 2012, the OpenStack Foundation was established to oversee the project's development and ensure its long-term governance as an open-source project.
Maturing Ecosystem (2013-2015): OpenStack continued to evolve, with new releases like Grizzly, Havana, Icehouse, and Juno. During this period, more projects were added to the ecosystem, covering areas such as networking (Neutron), block storage (Cinder), and identity (Keystone).
Enterprise Adoption (2016-2017): OpenStack gained significant traction among enterprises and service providers. Projects like Heat (orchestration) and Magnum (containers) were introduced to support cloud automation and container orchestration.
Continued Growth (2018-Present): OpenStack has continued to grow and evolve, with new projects and features being added regularly. The community releases new versions of OpenStack every six months, with each version introducing enhancements and improvements.
Openstack Releases: Currently running Openstack is release is "xena". Austin was the 1st Openstack release and it obsolete now. For more details check the links below:
https://docs.openstack.org/puppet-openstack-guide/latest/install/releases.html
https://releases.openstack.org/
Austin (2010): The first official release of OpenStack, code-named "Austin."
Bexar (2011): The second release, code-named "Bexar."
Cactus (2011): The third release, code-named "Cactus."
Diablo (2011): The fourth release, code-named "Diablo."
Essex (2012): The fifth release, code-named "Essex."
Folsom (2012): The sixth release, code-named "Folsom."
Grizzly (2013): The seventh release, code-named "Grizzly."
Havana (2013): The eighth release, code-named "Havana."
Icehouse (2014): The ninth release, code-named "Icehouse."
Juno (2014): The tenth release, code-named "Juno."
Kilo (2015): The eleventh release, code-named "Kilo."
Liberty (2015): The twelfth release, code-named "Liberty."
Mitaka (2016): The thirteenth release, code-named "Mitaka."
Newton (2016): The fourteenth release, code-named "Newton."
Ocata (2017): The fifteenth release, code-named "Ocata."
Pike (2017): The sixteenth release, code-named "Pike."
Queens (2018): The seventeenth release, code-named "Queens."
Rocky (2018): The eighteenth release, code-named "Rocky."
Stein (2019): The nineteenth release, code-named "Stein."
Train (2019): The twentieth release, code-named "Train."
Ussuri (2020): The twenty-first release, code-named "Ussuri."
Victoria (2020): The twenty-second release, code-named "Victoria."
Wallaby (2021): The twenty-third release, code-named "Wallaby."
Xena (2021): The twenty-fourth release, code-named "Xena."
Yoga (2022): The twenty-fifth release, code-named "Yoga."
Zuul (2022): The twenty-sixth release, code-named "Zuul."
OpenStack's modular architecture allows organizations to choose the components that best fit their cloud computing needs, making it a versatile and customizable platform for building private, public, and hybrid clouds. OpenStack is built using a modular architecture, where each component provides a specific cloud service. These components can be combined to create a custom cloud infrastructure tailored to the organization's needs. OpenStack is composed of multiple projects, each providing a specific cloud service.
- Multi-Tenancy: OpenStack supports multi-tenancy, allowing organizations to create isolated environments within the cloud infrastructure. This means that multiple users or projects can share the same cloud while maintaining security and resource separation.
- Open Source: OpenStack is released under an open-source license, making it freely available for anyone to use, modify, and contribute to. This open nature has led to a vibrant community of developers and users collaborating on its development.
- Integration and Compatibility: OpenStack is designed to integrate with various virtualization technologies, hardware vendors, and third-party tools. It can be used with different hypervisors, storage systems, and networking solutions.
- Private and Public Clouds: Organizations can use OpenStack to create private clouds within their data centers or deploy public cloud services to offer cloud resources to external customers or users.
- Hybrid Clouds: OpenStack can be part of a hybrid cloud strategy, where organizations combine private and public cloud resources to achieve flexibility and scalability
Here are some of the main components:
- Nova (Compute): Manages and orchestrates virtual machines (instances) on hypervisors. It provides features for creating, scheduling, and managing VMs.
- Swift (Object Storage): Offers scalable and durable object storage services for storing and retrieving data, including large files and unstructured data.
- Cinder (Block Storage): Manages block storage volumes that can be attached to instances. It provides persistent storage for VMs.
- Neutron (Networking): Handles networking services, including the creation and management of networks, subnets, routers, and security groups.
- Keystone (Identity): Manages identity and authentication services, including user management, role-based access control (RBAC), and token authentication.
- Glance (Image Service): Stores and manages virtual machine images (VM snapshots) that can be used to create instances.
- Horizon (Dashboard): A web-based user interface that provides a graphical way to manage and monitor OpenStack resources.
- Heat (Orchestration): Provides orchestration and automation services for defining and managing cloud application stacks.
- Ceilometer (Telemetry): Collects telemetry data, including usage and performance statistics, for billing, monitoring, and auditing.
- Trove (Database-as-a-Service): Manages database instances as a service, making it easier to provision and manage databases.
- Ironic (Bare Metal): Manages bare-metal servers as a service, allowing users to provision physical machines in the same way as virtual machines.
- Zaqar (Messaging and Queuing): Provides messaging and queuing services for distributed applications.
- Magnum (Container Orchestration): Orchestrates container platforms like Kubernetes to manage containerized applications.
Postman provides a user-friendly interface for building and sending API requests, inspecting responses, and automating API testing. Internally, Postman is a comprehensive software tool that facilitates the process of sending HTTP requests to APIs, receiving responses, and performing various tasks related to API testing, monitoring, and development. It operates through a combination of user interactions and underlying components. Postman simplifies the process of sending HTTP requests to APIs by providing a user-friendly interface, generating HTTP requests based on user input, and enabling users to work with API responses. It also supports more advanced features such as scripting, automation, and test execution for comprehensive API testing and monitoring. It's widely used by developers to
- Test APIs: Developers can use Postman to send requests to APIs and receive responses, making it easy to test how the API functions.
- Automate Tests: Postman allows you to create and automate test scripts to ensure that your APIs are working as expected. You can set up tests to validate the response data, headers, and more.
- Document APIs: You can use Postman to generate API documentation, which is useful for sharing information about how to use an API with others.
- Monitor APIs: Postman can be used to monitor APIs and receive alerts when issues or errors occur.
- Mock Servers: Postman provides the ability to create mock servers, which can simulate an API's behavior without the actual backend being implemented yet.
Here's how Postman is involved and invoked internally when working with the examples provided:
1) User Interface (UI): Postman provides a user-friendly graphical interface where users can create, manage, and send API requests. Users interact with this UI to input API details, such as request URLs, headers, parameters, and request bodies.
2) Request Configuration: When you create a request in Postman, you configure various aspects of the request, including the request method (e.g., GET, POST, PUT, DELETE), request URL, headers, query parameters, request body (if applicable), and authentication settings.
3) HTTP Request Generation: Postman internally generates the corresponding HTTP request based on the user's configuration. For example, if you configure a GET request to retrieve user data, Postman generates an HTTP GET request to the specified URL with the provided headers and parameters.
4) Request Sending: When you click the "Send" button within Postman, it sends the generated HTTP request to the target API endpoint using the configured settings (e.g., URL, headers, body). This request is sent via the HTTP protocol to the specified API server.
5) API Server Interaction: The HTTP request sent by Postman is received by the API server. The server processes the request based on the HTTP method, URL, and other request details. For example, in a RESTful API, a GET request may retrieve data, while a POST request may create new data.
6) Response Reception: After the API server processes the request, it sends an HTTP response back to Postman. This response includes data (e.g., JSON or XML) and metadata (e.g., status code, headers) generated by the server.
7) Response Handling: Postman receives the HTTP response and presents it to the user within its UI. The user can inspect the response content, status code, headers, and other details. Postman also provides tools for handling response data, such as extracting values or running tests.
8)Test Execution: Users can define tests and assertions within Postman using scripts (e.g., JavaScript). When a test script is defined, Postman internally executes the script and checks the results against the specified assertions.
9) Results Reporting: Postman provides feedback to the user about the outcome of the API request and any tests that were run. Users can view whether the request was successful, the response met the expected criteria, and any potential errors or issues.
10)Automation: Postman can be integrated into automated testing pipelines, continuous integration (CI) workflows, and monitoring systems. It can be invoked programmatically to run collections of requests, automate tests, and monitor APIs at specified intervals.
Examples: make sure you have access to a RESTful API that you want to test. Replace the URL, endpoints, and parameters with the appropriate values for your specific API.
1) GET Request to Retrieve Data . To retrieve data from an API using a GET request:
- GET https://api.example.com/users
2) GET Request with Query Parameters.To retrieve data with query parameters:
- GET https://api.example.com/users?id=123&name=John
3) POST Request to Create Data.To create data using a POST request with a JSON body:
- POST https://api.example.com/users
Headers:Content-Type: application/json
Body (JSON):{ "name": "Alice", "email": "alice@example.com"}4) PUT Request to Update Data.To update data using a PUT request with a JSON body:
- PUT https://api.example.com/users/123
Headers:
Content-Type: application/json
Body (JSON):
{
"name": "Updated Name",
"email": "updated@example.com"
}
5) DELETE Request to Remove Data. To delete data using a DELETE request:
- DELETE https://api.example.com/users/123
6) Headers and Authentication. You can add headers, such as authorization headers, to your requests. For example, to send an API key in the headers
- GET https://api.example.com/resource
Headers:
Authorization: Bearer YOUR_API_KEY7) Handling Response Data:After sending a request, you can inspect the response data. For example, to extract a specific value from the response, you can use JavaScript-like syntax in Postman's Tests tab:
// Extract the value of the "name" field from the JSON response
var jsonData = pm.response.json();
pm.environment.set("username", jsonData.name);
These are just some basic examples of how to use Postman to interact with RESTful APIs. You can create collections of requests, use variables, and write more complex tests to thoroughly test and validate your APIs.
Python code example that demonstrates how to make an HTTP GET request to a RESTful API using the popular requests library. In this example, we'll use the JSONPlaceholder API, which provides dummy data for testing and learning purposes:
import requests
# Define the API endpoint URL
api_url = "https://jsonplaceholder.typicode.com/posts/1"
try:
# Send an HTTP GET request to the API endpoint
response = requests.get(api_url)
# Check if the request was successful (status code 200)
if response.status_code == 200:
# Parse the JSON response
data = response.json()
# Print the response data
print("Title:", data["title"])
print("Body:", data["body"])
else:
print("HTTP Request Failed with Status Code:", response.status_code)
except requests.exceptions.RequestException as e:
# Handle any exceptions that may occur during the request
print("An error occurred:", e)
NOTE: We define the API endpoint URL (api_url) that we want to retrieve data from. In this example, we're fetching data for a specific post using its ID.
and use a try block to send an HTTP GET request to the API endpoint using requests.get(api_url).
We check the HTTP response status code. If it's 200, the request was successful, and we proceed to parse the JSON response.If the request was successful, we parse the JSON response using response.json() and print specific fields from the response (in this case, the post's title and body). If the request fails or encounters an exception, we handle it and print an error message.
OpenStack provides a set of RESTful APIs for managing cloud infrastructure resources. These APIs are used to create, manage, and interact with virtualized resources such as instances (virtual machines), volumes, networks, and more. Here are some common API endpoint examples with respect to OpenStack:
1) Identity (Keystone) API:
Authentication and token management.
Example: http://<OpenStack-IP>:5000/v3/
Compute (Nova) API:
2) Management of virtual machines (instances).
Example: http://<OpenStack-IP>:8774/v2.1/
Block Storage (Cinder) API:
3) Management of block storage volumes.
Example: http://<OpenStack-IP>:8776/v2/
Object Storage (Swift) API:
4) Storage and retrieval of objects (files and data).
Example: http://<OpenStack-IP>:8080/v1/
Image (Glance) API:
5) Management of virtual machine images (VM snapshots).
Example: http://<OpenStack-IP>:9292/v2/
Network (Neutron) API:
6) Management of network resources, including routers, subnets, and security groups.
Example: http://<OpenStack-IP>:9696/v2.0/
Orchestration (Heat) API:
7) Orchestration of cloud resources through templates.
Example: http://<OpenStack-IP>:8004/v1/
Telemetry (Ceilometer) API:
8) Collection of usage and performance data.
Example: http://<OpenStack-IP>:8777/v2/
Dashboard (Horizon) API:
9) Web-based user interface for OpenStack services.
Example: http://<OpenStack-IP>/dashboard/
Placement (Placement) API:
10) Management of resource placement and allocation.
Example: http://<OpenStack-IP>:8778/
These are just some examples of the core OpenStack APIs and their respective endpoint URLs.
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To check if a user exists in your OpenStack environment, you can use the Identity (Keystone) API, which manages authentication and user-related operations. Specifically, you can make a request to the Keystone API to list users and then check if the desired user is in the list. Here are the general steps to do this:
Step 1 :Authenticate with Keystone:
Before making any requests to the Keystone API, you need to authenticate. Typically, this involves sending a POST request with your credentials to the Keystone authentication endpoint. You'll receive a token in response, which you can use to make subsequent API requests.
List Users:
Step 2 : Make a GET request to the Keystone API's user listing endpoint to retrieve a list of all users in the OpenStack environment.
Example API endpoint for listing users: http://<OpenStack-IP>:5000/v3/users
Include the authentication token in the request headers.
Check User Existence:
Step 3 : After receiving the list of users, you can iterate through the user data and check if the desired user exists by comparing usernames, IDs, or other unique identifiers.
Here's a Python example using the requests library to check if a user exists in Keystone:
import requests
# Keystone authentication endpoint
auth_url = "http://<OpenStack-IP>:5000/v3/auth/tokens"
# Keystone user listing endpoint
users_url = "http://<OpenStack-IP>:5000/v3/users"
# Replace with your OpenStack credentials
auth_data = {
"auth": {
"identity": {
"methods": ["password"],
"password": {
"user": {
"name": "your_username",
"domain": {"name": "your_domain"},
"password": "your_password"
}
}
}
}
}
# Authenticate and get a token
response = requests.post(auth_url, json=auth_data)
if response.status_code == 201:
token = response.headers["X-Subject-Token"]
# List all users
headers = {"X-Auth-Token": token}
response = requests.get(users_url, headers=headers)
if response.status_code == 200:
users = response.json()["users"]
# Check if the user exists
target_user = "desired_username"
user_exists = any(user["name"] == target_user for user in users)
if user_exists:
print(f"User {target_user} exists.")
else:
print(f"User {target_user} does not exist.")
else:
print("Failed to list users.")
else:
print("Authentication failed.")
This example demonstrates how to authenticate with Keystone, list users, and check if a specific user exists by comparing usernames. Replace placeholders with your OpenStack-specific values and adjust the code as needed for your environment
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OpenStack service overview:
Nova , Cinder, Swift and Neutron -these OpenStack services together provide a comprehensive cloud computing platform. Nova manages compute resources, Cinder offers block storage, Swift provides object storage, and Neutron handles networking, enabling organizations to build and manage private and public clouds tailored to their specific needs.
Nova (OpenStack Compute): Nova is the core compute service in OpenStack. It manages the creation, scheduling, and management of virtual machines (VMs) in a cloud environment. Nova is hypervisor-agnostic, supporting various virtualization technologies, and it provides features for live migration, scaling, and resource management.
Cinder (OpenStack Block Storage): Cinder is the block storage service in OpenStack. It offers block-level storage volumes that can be attached to VMs. Users can create, manage, and snapshot these volumes, making it suitable for data persistence in applications like databases.
Swift (OpenStack Object Storage): Swift is the object storage service in OpenStack. It is designed for the storage of large amounts of unstructured data, such as images, videos, and backups. Swift provides scalable, redundant, and highly available storage with easy-to-use APIs.
Neutron (OpenStack Networking): Neutron is the networking service in OpenStack. It enables users to create and manage networks, subnets, routers, and security groups for VMs. Neutron supports various network configurations, including flat networks, VLANs, and overlay networks, allowing for flexibility in network design.
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Key Differences between Cinder and swift : The object storage and block storage serve different purposes and have distinct access methods. Object storage is well-suited for handling unstructured data and large-scale content distribution, while block storage is preferred for applications requiring direct control over data blocks and high performance. Organizations often choose between these storage types based on their specific use cases and storage needs.
Access Level: Object storage uses a higher-level access method, where data is accessed and managed as whole objects using unique identifiers. Block storage provides lower-level access, treating data as raw blocks.
Use Cases: Object storage is ideal for storing large amounts of unstructured data and content distribution, while block storage is suited for applications requiring direct control over storage blocks.
Scalability: Object storage is known for its horizontal scalability and ease of expansion, whereas block storage scalability may require more planning and management.
Data Management: Object storage systems often manage data redundancy and durability internally, while block storage may rely on external solutions or the application to manage data redundancy.
Data Retrieval: Object storage is optimized for read-heavy workloads and large-scale data distribution, while block storage is designed for high performance and low-latency access.
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Ceph:
Ceph is an open-source, distributed storage system designed for both object and block storage. It is known for its flexibility, scalability, and ability to provide a unified storage platform. Ceph is often used in cloud computing environments, data centers, and high-performance computing (HPC) clusters.
Key components and features of Ceph include:
Object Storage (RADOS Gateway): Ceph provides object storage capabilities through its RADOS (Reliable Autonomic Distributed Object Store) Gateway. This allows users to store and retrieve objects using a RESTful API compatible with Amazon S3 and Swift.
Block Storage (RBD): Ceph's RADOS Block Device (RBD) allows users to create block storage volumes that can be attached to virtual machines or used as raw block storage. RBD is often integrated with virtualization platforms like KVM.
Scalability: Ceph scales seamlessly from a few nodes to thousands of nodes by distributing data across OSDs (Object Storage Daemons) and MONs (Monitor Daemons). This scalability makes it suitable for large-scale storage deployments.
Data Redundancy: Ceph replicates data across multiple OSDs to ensure redundancy and high availability. It uses a CRUSH algorithm to distribute data efficiently.
Self-Healing: Ceph can automatically detect and recover from hardware failures or data inconsistencies. It continuously monitors data integrity.
Unified Storage: Ceph provides a unified storage platform that combines object, block, and file storage, allowing users to access data in various ways, depending on their requirements.
Community and Ecosystem: Ceph has a vibrant open-source community and a wide ecosystem of tools and projects that integrate with it. This includes interfaces for OpenStack integration.
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Neutron, the networking component of OpenStack, plays a crucial role in creating and managing networking resources within a cloud infrastructure.
Here are some interesting factors and capabilities related to Neutron:
Network Abstraction: Neutron abstracts network resources, allowing users to create and manage virtual networks, subnets, routers, and security groups through APIs or the dashboard. This abstraction simplifies complex networking tasks and provides a consistent interface.
Multi-Tenancy: Neutron supports multi-tenancy, enabling the isolation of network resources between different projects or tenants. This ensures that one tenant's network activities do not impact another's.
Pluggable Architecture: Neutron follows a pluggable architecture, allowing users to integrate with various networking technologies and solutions. This includes support for different plugins and drivers, enabling compatibility with a wide range of network devices and services.
Software-Defined Networking (SDN): Neutron can be used in conjunction with SDN controllers and solutions to provide advanced network automation, programmability, and flexibility. SDN allows for the dynamic configuration of network services and policies.
Networking Interfaces: Neutron allows the creation of various types of networking interfaces for virtual machines, including:
Port: Neutron manages ports, which represent virtual interfaces connected to a network. VMs attach to ports to access the network.
Router: Routers connect different subnets and provide inter-subnet routing. Neutron manages router interfaces and routing rules.
Floating IPs: Floating IPs provide external network access to VMs. Neutron can assign floating IPs dynamically or statically.
Bonding and Teaming: Neutron can manage bonded network interfaces (NIC bonding) for redundancy and increased network bandwidth. This is especially useful for ensuring high availability and load balancing of VMs.
Security Groups: Neutron's security groups feature allows users to define firewall rules and policies to control incoming and outgoing traffic to VMs. It enhances network security within the cloud environment.
L3 and L2 Services: Neutron supports Layer 3 (routing) and Layer 2 (bridging) services. This flexibility enables complex network topologies and scenarios.
Interoperability: Neutron integrates with various network technologies, including VLANs, VXLANs, GRE tunnels, and more. It provides interoperability with physical network infrastructure and external networks.
Communication Between VMs: Neutron ensures that VMs can communicate with each other within the same network or across networks using routing. It manages the routing tables and connectivity.
Load Balancing as a Service (LBaaS): Neutron offers LBaaS, allowing users to create and manage load balancers to distribute traffic among multiple VMs or instances.
High Availability (HA): Neutron can be configured for high availability, ensuring network services remain operational even in the event of network node failures.
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Containerization in OpenStack involves deploying and managing containers within an OpenStack cloud environment. This allows users to run containerized applications and microservices alongside traditional virtual machines (VMs).
Here's a step-by-step explanation of the design and components involved in containerization within OpenStack:
1. Container Orchestration Framework: OpenStack supports various container orchestration frameworks, with Kubernetes being one of the most popular choices. Kubernetes helps manage the deployment, scaling, and operation of application containers. It serves as the foundation for container orchestration in an OpenStack environment.
2. Container Runtime: Containers are run using a container runtime, such as Docker or containerd. This runtime manages the execution of containerized applications and provides isolation between containers. In an OpenStack-based containerization setup, a container runtime is installed on each compute node in the OpenStack cluster.
3. OpenStack Components:
- Nova (Compute Service): Nova is responsible for managing compute resources, including VMs and, in a containerized environment, bare metal servers. It can provision servers specifically for running containers alongside traditional VMs.
- Neutron (Networking Service): Neutron handles networking and connectivity for containers. It ensures that containers can communicate with each other, VMs, and external networks.
- Cinder (Block Storage Service): Cinder provides block-level storage for containers when persistent storage is required. Containers can use Cinder volumes for data storage.
4. Magnum (Container Orchestration Service): OpenStack Magnum is a dedicated service for managing container orchestration clusters, such as Kubernetes, within the OpenStack cloud. It simplifies the deployment and management of container orchestration platforms.
5. Heat (Orchestration Service): Heat is an orchestration service in OpenStack that enables the automated deployment and scaling of infrastructure resources, including containers. It allows users to define templates describing the desired container infrastructure and then deploys and manages the resources accordingly.
6. Glance (Image Service): Glance is responsible for storing and managing container images. Containers are typically built from base images, and Glance helps manage these images within the OpenStack environment.
7. Keystone (Identity Service): Keystone provides authentication and authorization services for containerized applications and services. It ensures that only authorized users and services can access containers and container orchestration platforms.
8. Container Networking and Storage Plugins: In an OpenStack-based containerization environment, specialized networking and storage plugins are often used to integrate container networking and storage with OpenStack services. These plugins enable efficient communication and data storage for containers.
9. User Interface: Users interact with the containerization platform through the OpenStack dashboard (Horizon) or through the command-line interface (CLI). They can deploy and manage containers, container orchestration clusters, and associated resources.
10. Monitoring and Logging: Containerized applications generate logs and require monitoring for performance and resource usage. OpenStack can be integrated with monitoring and logging solutions like Prometheus, Grafana, and ELK (Elasticsearch, Logstash, and Kibana) to provide insights into containerized workloads.
11. External Services Integration: Containers often need to interact with external services and APIs. OpenStack allows for integration with external services through the use of network configurations, load balancers, and other relevant components.
In summary, containerization in OpenStack involves a combination of OpenStack services, container orchestration frameworks like Kubernetes, container runtimes, and specialized plugins to provide a seamless environment for deploying and managing containerized applications alongside traditional VMs within an OpenStack cloud infrastructure. This setup offers flexibility, scalability, and isolation for running containerized workloads in a cloud environment.
