What Is Cloud Orchestration?

1. Introduction

Cloud orchestration is a crucial element of managing modern software and its architecture. It enables the automatic administration of infrastructure, resource monitoring, scalability, and elasticity. Moreover, orchestration enables organizations to use their resources more effectively, minimize the costs of cloud environments, and simplify their administration.

In this tutorial, we’ll have a detailed discussion of the fundamentals of cloud orchestration, its pros and cons, and how it differs from cloud automation.

2. Definition and Structure

Cloud orchestration is a process of automated resource management, like virtual machines, containers, networks, or databases. Thus, it enables coordination, allocation, and monitoring of complex cloud environments. In other words, cloud orchestration provides the possibility of automated control of intricate cloud-related processes, such as application deployment or scaling.

Therefore, organizations can simplify and automate environment administration. Accordingly, this minimizes manual work and maximizes reaction time to changes.

Cloud orchestration connects three main operational areas:

• DevOps: integrating development and operational teams to automate processes and deliver software faster
• ITOps: managing IT infrastructure to ensure the stability and security of systems
• CloudOps: managing cloud resources and operations, including scaling and cost optimization

Each operational branch has different responsibilities for software and infrastructure. In particular, these comprise development, deployment, monitoring, management, and maintenance.

3. Key Features

Orchestration in the cloud relies on a few pivotal characteristics for the efficient management of resources and applications in a cloud-related environment.

3.1. Automation

To start with, one of the most important features is automation. Automation removes the need for manual intervention in the management of cloud resources.

Specifically, automation enables self-operation for many processes and activities related to infrastructure management. Therefore, it eliminates the need for manual operations. Thus, it results in much faster deploying, scaling, and updating of resources, significantly increasing operational processes’ speed.

3.2. Workload Management

The next crucial aspect of cloud orchestration is workload management. Orchestration enables the creation of complicated processes across various infrastructure parts, such as virtual machines, containers, or databases. Further, all components can cooperate in a seamless manner without any disturbance.

Moreover, orchestration makes it possible to integrate different cloud services. Modern cloud providers consist of many platforms and tools for different purposes. By using orchestration, it’s possible to integrate both cloud and on-premise services. Thus, creating hybrid and consistent environments that work seamlessly.

3.3. Scalability

Another pivotal feature of cloud orchestration is scaling. Organizations that use scaling in their development can dynamically change resources depending on changing workloads and needs. In other words, the infrastructure can automatically increase resources such as virtual machines or containers in the event of increased traffic.

On the other hand, when the load is dropping, the infrastructure can decrease resources. Due to this flexibility, organizations can efficiently use their available resources and optimize costs.

3.4. Infrastructure State

Finally, one of the pivotal features when it comes to cloud orchestration is monitoring and management of the infrastructure state. Orchestration in the cloud provides tools that enable infrastructure monitoring in real-time. Therefore, we have complete visibility into the infrastructure, helping to understand performance, detect issues, and resolve them promptly.

4. Workflow

The orchestration workflow consists of a few crucial stages. Each phase extensively uses designated orchestration components that perform various tasks. However, some components are used in each phase during the whole orchestration workflow and the application lifecycle:

Let’s go through this general visualization of the orchestration workflow.

4.1. Request for Resource Allocation

The orchestration process starts with a third party requesting resources. Depending on the context, resources can be requested by a user, administrator, or a system such as AWS Lambda or Kubernetes.

The request can include running new virtual machine instances, allocating memory, deploying a new application or its instance, and so on. The request reaches the crucial component called the orchestration module.

4.2. Resource Allocation

Going further, the orchestration module allocates appropriate resources, such as virtual machines, containers, memory, or other computing power, such as AWS EC2, Azure VM, or Google Cloud instances.

The goal is to provide high availability of resources that enable running applications or services in the cloud. Thus, the choice of tools is pivotal for the efficiency and scalability of the whole system.

4.3. Task Automation

Moreover, besides allocating resources, the orchestration workflow can automate tasks related to configuring and managing these resources. This results in significant improvement in deployment speed and eliminates the risk of human errors.

Tools such as Jenkins, Chef, or Puppet enable running processes and pipelines that can install applications or whole systems, configure them, or update them automatically.

4.4. Monitoring and Adjustment

Once the software is deployed and running, orchestration provides tools for monitoring all components in real-time. Therefore, it’s capable of checking their performance, load, or compute utilization. In this case, we can talk about popular tools such as Prometheus, Datadog, and Grafana.

Furthermore, such an approach makes the software highly available and less error-prone. Additionally, developer teams can focus on the innovation of the product, gaining more time for improving features rather than manual monitoring and repairing infrastructure problems.

4.5. Scaling

As already mentioned, the orchestration in a cloud and its monitoring tools can automatically adjust depending on the needs. To be precise, the ability to increase resources during peak time and scale down them during off-peak time is called elasticity.

Each cloud provider often has its own scaling tools. We can mention, for example, AWS Auto Scaling, Google Cloud Autoscaler, or Kubernetes Autoscaler. Elasticity results in the optimization of costs and efficiency of computational usage, which is crucial in dynamic cloud environments.

4.6. Application Resource Assignment

Finally, after allocating and configuring resources, the orchestration module assigns them to the appropriate tasks or services, such as Kubernetes Pods, Docker containers, or AWS Lambda functions. Such a dynamic and automatic process enables them to immediately start using the newly allocated resources. Therefore, we ensure a fast and flawless start of the system in the cloud environment. The orchestration workflow is now completed.

5. Summary

In this article, we discussed cloud orchestration as a core process for the automated management of IT infrastructure resources that brings together the areas of DevOps, ITOps, and CloudOps. It enables efficient administration of cloud computing environments through process automation, flexible load management, and dynamic scalability.

As we saw, the orchestration workflow includes the initial resource request, resource allocation, task automation, and monitoring. To that end, tools and providers such as Kubernetes or AWS make it easier for organizations to optimize costs and increase operational efficiency, minimizing manual work for administrators.