Cloud automation processes can provision and modify cloud resources, such as compute instances and storage buckets, without human intervention. There are several types of automation. Some involve manual oversight and approval, others perform tasks in the background on a predefined schedule or triggered by certain events, and others provide users with self-service access to cloud resources.
Cloud automation consists of various software tools that interact with hardware resources. The software tools implement policies that define how to balance and allocate workloads, match the appropriate compute nodes to the available hardware, and sustain activities. Additionally, cloud automation tools push out alerts about errors and use system-level information and telemetry to inform decisions about performance optimization and workload placement.
This is part of our series of articles about cloud optimization.
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Manually deploying and operating enterprise workloads is complex and time-consuming work. It involves many repetitive tasks, including configuration, sizing, provisioning cloud resources like virtual machines (VMs), setting up networking, load balancing, and managing scaling and high availability.
These repetitive, manual processes can be effective. However, they can also be inefficient and cause inadvertent errors that require troubleshooting efforts, further delaying workload availability. They can also expose the workload to security vulnerabilities that put the entire enterprise at risk. Cloud automation eliminates these manual processes. You can set up cloud automation by using orchestration and automation tools that can run on top of your virtualized environment.
Cloud automation and orchestration are two different processes that often work together but perform different roles. Cloud automation helps automate a single task. Orchestration automates several tasks—it is a process that arranges multiple automated processes.
Cloud automation vs. orchestration: an example
Applications consist of many different components, including backend and frontend components. For example, an application can include a virtual server, a database, a user interface (UI), and configuration files.
You might need to update each application component independently. If you use cloud automation, you can automate each of these as a separate process. However, updating each component independently of the others can lead to version conflicts and misconfigurations.
Cloud orchestration helps you plan and manage the order of each automated update to ensure the deployment process runs as smoothly as possible. As part of the orchestration process, one automation can trigger another one.
Cloud orchestration is ideal for multi-cloud implementations. It is difficult to track multiple scalable workloads manually. Enterprises looking for efficiency can leverage cloud automation and orchestration to manage multi-cloud workloads.
Common cloud automation use cases include:
Related content: Read our guide to cloud optimization (coming soon)
Cloud platforms provide built-in visibility and discoverability features. You can tailor various aspects to your needs when using a single vendor or environment. However, when implementing multi-cloud, hybrid, and multi-subscription and account environments, you cannot gain complete visibility because these built-in tools cannot aggregate all data into one view.
Instead of manually managing disparate cloud environments, you can automatically merge all data in one place using cloud management or monitoring platforms. Many cloud tools and add-ons integrate with Kubernetes, for example, Prometheus, Grafana, and Jaeger.
Hybrid cloud and multi-cloud implementations enable you to scale quickly to handle excess or peak capacity. You can manually add cloud resources when needed, but this is inefficient. Instead, you can set up autoscaling, a core tenet of cloud automation.
Autoscaling lets you define rules that tell the automated program how to scale your computing, storage, and database resources. It helps ensure cloud native applications remain available and with sufficient resources to prevent performance crashes or issues.
Related content: Read our guide to cloud capacity
Using tags can help you sort, filter, and automate your cloud resources. Tags are identifiers or labels attached to instances within the cloud infrastructure. You can use tags to add custom metadata to complement existing metadata like size, IP information, region, and VPC.
Ideally, you should plan how you want to find and group resources and instances, using standardizing tags to reduce future complexities. Tools like Terraform and Ansible can help you automate and maintain tagging standards.
Automation can help you monitor and optimize your cloud costs. Most cloud vendors offer pay-as-you-go billing that requires careful tracking to avoid inflated bills. Tracking costs is especially important when using a multi-cloud environment.
You can employ automated policies and alerts to direct users to the relevant resource types, inform stakeholders when usage spikes, and disable inactive resources. Cloud vendors offer various reporting and scheduling tools and also allow integration with third-party tools.
While public cloud providers offer native tools for some cloud automation and cost optimization, and even provide recommendations for potential cost reduction, they stop short of actually implementing any of those optimizations for you.
This is where Spot by NetApp’s portfolio can help. Spot not only provides comprehensive visibility into what is being spent on your cloud compute and by whom, but also:
for up to 20 instances