What Is Cloud Automation?
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.
In this article:
- Why Are Cloud Automation Tools Important?
- Cloud Automation vs. Cloud Orchestration
- Cloud Automation Use Cases
- Cloud Computing Automation Best Practices
- Cloud Cost Optimization with Spot by NetApp
Why Are Cloud Automation Tools Important?
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 vs. Cloud Orchestration
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.
Cloud Automation Use Cases
Common cloud automation use cases include:
- Provisioning infrastructure with automated tools—for example, automation saves time when configuring multiple virtual servers. You can use an infrastructure-as-code (IaC) cloud automation tool to create templates defining and applying each server’s configuration. This approach works for other cloud resources, including networking and storage volumes. IaC tools are especially suited to the cloud, with cloud vendors often providing vendor-specific solutions.
- Managing workloads—For instance, you can configure monitoring tools to track a workload’s performance. Automated tools can respond to alerts and implement scaling or load-balancing to improve performance or optimize resources.
- Enabling DevOps work processes—Agile methodologies like DevOps rely on automated tools to rapidly deploy and scale resources and test software. The tools release the resources for reuse after testing. Public cloud providers typically offer various automation tools.
- Version control and visibility—by consistently implementing cloud automation, organizations create an audit trail that clearly shows which cloud resources are running and what changes are being made.
- Cloud optimization—automation tools can help adjust cloud workloads so they provide the best performance and highest business value for the lowest cost.
Related content: Read our guide to cloud optimization
Cloud Computing Automation Best Practices
Promote Visibility Across Environments
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.
Determine the Optimal Scaling Strategy
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
Organize Cloud Automation With Tags
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.
Develop a Plan for Monitoring and Optimizing Costs
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.
Cloud Cost Optimization with Spot by NetApp
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:
- Generates an average saving of 68% by showing you exactly where you can use either EC2 spot instances or reserved capacity (RIs and Savings Plans) to save costs. It lets you reliably automate workload optimization recommendations in just a few clicks.
- Guarantee continuity for spot instances, ensuring even production and mission-critical applications can safely run on spot instances, using predictive algorithms and advanced automation to guarantee workload continuity.
- Manage RIs and Saving Plans portfolios, providing maximum utilization and ROI with minimal risk of financial lock-in and cloud waste.
- Maximize savings for DevOps teams running Kubernetes with proven machine learning and automation to continuously determine and deploy the most balanced and cost-effective compute resources for your container clusters.