Google Cloud compute offerings
1. Google Cloud compute offerings
As organizations design for the future and start running more compute workloads in Google Cloud, it's important to be aware of the options available. Google offers a range of computing services, let's explore each. The services are: Compute Engine, GKE, App Engine, Cloud Run, and Cloud Run functions. At the end of this lesson, you'll understand why people choose each. The first is Compute Engine. Compute Engine is an IaaS offering, or infrastructure as a service, which provides compute, storage, and network virtually that are similar to physical data centers. Compute Engine provides access to predefined and customized virtual machine configurations. At the time this training was developed, VMs could be as large as 416 vCPUs with more than 12 TB of memory. Virtual machines require block storage, and Compute Engine offers two main choices: Persistent disks and local SSDs. Persistent disks offer network storage that can scale up to 257 TB, and can disk snapshots for backup and mobility. Alternatively, local SSDs enable high input/output operations per second. Compute Engine workloads can be placed behind global load balancers that support autoscaling. They offer a feature called managed instance groups with which resources can be defined to automatically deploy to meet demand. Compute Engine costs can be controlled with the help of per-second billing. This means that when compute resources are deployed for short periods of time, like with batch processing jobs, costs can stay low. Compute Engine also offers preemptible virtual machines, which provide significantly cheaper pricing for workloads that can be interrupted safely. Compute Engine is a popular choice for developers because: It provides complete control over infrastructure since operating systems can be customized, and it can run applications that rely on a mix of operating systems. On-premises workloads can easily be lifted and shifted to Google Cloud without needing to rewrite applications or make any changes. And it's the best option when other computing options don't support your application or requirements. Google Cloud's second compute offering, and the focus of this course, is Google Kubernetes Engine. GKE runs containerized applications in a cloud environment, as opposed to on an individual virtual machine, like Compute Engine. A container represents code packaged with all its dependencies. The third computing service offered by Google is App Engine. App Engine is a fully managed PaaS offering, or platform as a service. PaaS offerings bind code to libraries that provide access to the infrastructure application needs. This allows more resources to be focused on application logic instead of deployment. This means developers can just upload code and App Engine will deploy the required infrastructure. It supports popular languages like Java, Node.js., Python, PHP, C#, .NET, Ruby, and Go and can also be used to run container workloads. App Engine is closely integrated with Cloud Monitoring, Cloud Logging, Cloud Profiler, and Error Reporting. App Engine also supports version control and traffic splitting. App Engine is a good choice for developers that: Want to focus on writing code. Want to focus on building applications instead of deploying and managing the environment. And don't need to build a highly reliable and scalable infrastructure. Some of the most common App Engine use cases include: websites, mobile app and gaming backends, and a method to present a RESTful API, which is an application program interface that resembles the way a web browser interacts with a web server, to the internet. App Engine makes them easy to operate. Another compute option offered by Google Cloud is Cloud Run. Cloud Run is a managed compute platform that runs stateless containers through web requests or Pub/Sub events. Cloud Run is serverless. That means it removes all infrastructure management tasks so you can focus on developing applications. It's built on Knative, an open API and runtime environment built on Kubernetes that gives you freedom to move your workloads across different environments and platforms. It can be fully managed on Google Cloud, on Google Kubernetes Engine, or anywhere Knative runs. Cloud Run is fast. It can automatically scale up and down from zero almost instantaneously, and it charges only for the resources used, calculated down to the nearest 100 milliseconds, so you never pay for over-provisioned resources. And finally, there is Cloud Run functions. Cloud Run functions is a lightweight, event-based, asynchronous compute solution for creating small, single-purpose functions that respond to cloud events, without the need to manage a server or a runtime environment. It executes code in response to events, like when a new file is uploaded to Cloud Storage. It's also a completely serverless execution environment. Cloud Run functions is often referred to as functions as a service. Simply upload code written in Node.js, Python, Go, Java, .Net Core, Ruby, or PHP; and Google Cloud will automatically deploy the appropriate computing capacity to run that code. These functions can be used to construct application workflows from individual business logic tasks. Cloud Run functions can also be used to connect and extend cloud services. You're billed to the nearest 100 milliseconds, but only while your code is running. Cloud Run functions also provides a perpetual free tier, so many Cloud Run function use cases can be free of charge. What are common Cloud Run functions? It can be part of a microservices application architecture. It is used to build simple, serverless mobile or IoT backends or integrate with third-party services and APIs. Files uploaded to a Cloud Storage bucket can be processed in real time. Similarly, the data can be extracted, transformed, and loaded for querying and analysis. And it can be part of intelligent applications such as virtual assistants, video or image analysis, and sentiment analysis.2. Let's practice!
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