What type of cloud computing service provides raw compute, storage, and network

This article explains the google cloud computing services. The following picture shows that the computing services are laying in the middle layer of the Google Cloud Infrastructure.

Which Computing Services are in GCP?

Different types of compute infrastructure are available through GCP. The best option for your data engineering needs may vary depending on a number of variables. You should be familiar with the following four important compute options:

• Compute Engine
• Google Kubernetes Engine (GKE)
• App Engine
• Cloud Functions

• Compute Engine is an IaaS (Infrastructure as a service), which provides raw compute, storage and network capabilities organized virtually into resources that are similar to physical data centers.
• Secure and customizable compute service that lets you create and run VMs (Virtual Machines) on Google’s infrastructure.
• It provides maximum flexibility for those who prefer to manage server instances themselves.
• Similar to EC2 (Elastic Cloud Computing) in AWS.

2. Google Kubernetes Engine (GKE)

• GKE is a managed Kubernetes service, which runs containerized applications in a Cloud environment, as opposed to on an individual virtual machine like Compute Engine.
• Kubernetes is an open-source platform for container orchestration, which was developed by Google and is now widely used for deploying containerized applications.
• A container represents code packaged up with all its dependencies and solves the problem of “BUT, THE CODE IS WORKING ON MY MACHINE!”.

• Theoretically, You could run your Kubernetes cluster by deploying it to VMs in Compute Engine, but then you will have the complete responsibility for managing the cluster. Otherwise, Google can do this job for you with GKE.
• Start quickly with single-click clusters and scale up to 15000 nodes
• Similar to EKS (Elastic Kubernetes Service) in AWS.

3. App Engine

• A fully managed PaaS (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.
• It allows developers to focus on application development while minimizing their need to support the infrastructure that runs their applications.
• App Engine has two versions: App Engine Standard and App Engine Flexible.
• App Engine Standard supports many programming languages such as Python, Go, Java, PHP, and Javascript (NodeJs).
• App Engine Flexible runs Docker containers.
• Similar to Elastic Beanstalk in AWS.

4. Cloud Function

• Cloud Functions is a serverless compute service for running code in response to events that occur in the cloud.
• Cloud Functions executes code in many programming languages and responds to events, like when a new file is uploaded to Cloud Storage.
• Events such as writing a message to a Cloud Pub/Sub topic
  or uploading a file to Cloud Storage can trigger the execution of a Cloud Function.
• Cloud Functions also respond to events in HTTP, Firebase, and Stackdriver Logging.
• It’s a completely serverless execution environment, often referred to as FaaS (Functions as a service).
• Similar to Lambda in AWS.

Where does all the processing power come from?

• According to Stanford University’s 2019 AI Index Report, before 2012, artificial intelligence results tracked closely with Moore’s Law, with computing power doubling every two years.
• The report states that since 2012, computing power has been doubling approximately every three and half months.
• This means that hardware manufacturers have run up against limitations and CPUs, which are central processing units, and GPUs, which are graphics processing units, can no longer scale to adequately reach the rapid demand for machine learning.

• TPUs are Google’s custom-developed application-specific integrated circuits used to accelerate machine learning workloads. TPUs act as domain-specific hardware as opposed to general-purpose hardware with CPUs and GPUs.
• This allows for higher efficiency by tailoring architecture to meet the computation needs in a domain such as matrix multiplications in machine learning.
• Cloud TPUs have been integrated across Google products. In this state-of-the-art hardware and supercomputing technology is available with Google Cloud products and services.

• With TPUs, the computing speed increases more than 200 times.
• This means that instead of waiting 26 hours for results with a single state-of-the-art GPU, you’ll only need to wait 7.9 minutes for a full Cloud TPU v2 Pod to deliver the same results.