Compute Engine
Google Cloud Compute Engine, a service that lets you run virtual machines (VMs) on Google’s global infrastructure.
Here is a summary of the key takeaways:
Core Benefits
Flexibility & Scale: You can easily scale resources up or down based on your needs.
Cost-Efficient: It follows a pay-as-you-go model, meaning you only pay for the resources you actually use.
Customization: It supports any preferred operating system and leverages Google's high-end security and networking hardware.
Common Use Cases
Development: Hosting websites and creating testing/dev environments.
Heavy Workloads: Processing massive datasets, running scientific simulations, and training machine learning models.
Reliability: Serving as a backup for disaster recovery by replicating on-premises workloads to the cloud.
Four machine families offered by Google Cloud’s Compute Engine, which are categorized based on their balance of CPU and memory.
Core Concept: Machine Families vs. Types
Machine Families: Broad categories tailored for specific workloads.
Machine Types: Specific configurations within those families that define the amount of vCPU, memory, and storage.
The Four Google Cloud Machine Families
Key Takeaway
Choosing a machine family depends entirely on your workload requirements—whether you need a cost-effective all-rounder (General-purpose) or specialized power for AI and data processing (Accelerator or Memory-optimized).
Virtualization & Computing
Virtual machines are "chopped-up" segments of physical hardware that allow multiple users to share one system.
Flexibility: VMs are decoupled from physical hardware, allowing them to move or scale without downtime.
Access: Users can SSH into simple VMs for basic servers or customize complex clusters.
Performance: Being "close to bare metal" means minimal abstraction layers, giving your code direct access to the physical CPU's power.
Storage & Disks
Storage is the foundation of a VM instance, acting like a highly available, replicated physical disk.
Types: * Standard (HDD): Good for large sequential operations and cost-effective bulk storage.
SSD: High performance for random operations with no moving parts.
Local SSD: Offers the highest possible performance by being physically attached to the host.
Performance: Disk speed is directly tied to IOPS (Input/Output operations per second). Importantly, disk size and performance are correlated; larger disks are faster.
Constraints: You can increase disk size at any time, but you cannot decrease it. Disks must be in the same "zone" as the VM instance to attach.
Data Management: Snapshots vs. Images
While both act as templates for new disks, they serve different purposes:
Snapshots: Used for backups. They use "differential storage" (only saving changes since the last backup) to allow you to restore a disk to a specific point in time.
Images: Used as starting templates for deploying new disks across an organization.
Scalability & Instance Groups
Instance groups allow you to manage a collection of VMs as a single entity.
Autoscaling: Automatically adds VMs when traffic or CPU usage spikes and removes them when demand drops to save costs.
Self-Healing: If a single VM becomes unresponsive, the system marks it as "dead" and replaces it automatically.
Rolling Updates: Allows you to deploy new software to a fraction of your fleet at a time, ensuring the system remains stable if the new update has bugs.
Create a Compute engine instance - search for Compute Engine
Click into link
Hit Create Instance
Select Machine Size
Price varies depending on Machine configurations
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