Modernizing Infrastructure and Applications with Google Cloud · Topic 2 of 5
Compute Terms and Compute Engine
Compute terms for Cloud Digital Leader: VMs, containers, microservices, serverless, Spot VMs, Kubernetes, autoscaling, load balancing, and Compute Engine value.
- 4 min
- VMs include a full OS; containers package an app and share the host kernel.
- Microservices are small independent services; a monolith is one unit.
- Serverless: no servers to manage, pay per use.
What it is
Compute is the processing power that runs applications. In the cloud it comes in several forms, from virtual machines you manage to serverless services that manage everything for you. Compute Engine is Google Cloud's service for creating and running virtual machines on Google's infrastructure. The exam expects you to define the terms and explain the business value of Compute Engine.
How it works
Compute terms
| Term | Meaning |
|---|---|
| Virtual machine (VM) | A software computer with its own operating system, running on a physical server shared with other VMs |
| Containerization and containers | Packaging an application with everything it needs (libraries, settings) so it runs the same anywhere; containers share the host's operating system kernel, so they are lighter and start faster than VMs |
| Applications and microservices | A monolithic application is one large unit; microservices split it into small, independent services that each do one job and can be changed and scaled separately |
| Serverless computing | You deploy code or containers and the provider runs, scales and maintains the servers; you pay only when it runs |
| Spot VMs | Spare Compute Engine capacity at a deep discount; Google can reclaim (preempt) them at short notice, so they suit fault-tolerant and batch work |
| Kubernetes | Open source system, started at Google, that deploys, scales and manages containers across many machines |
| Autoscaling | Automatically adding or removing resources as demand changes |
| Load balancing | Spreading incoming traffic across several resources so none is overloaded, and routing around failed ones |
| Managed services | Services where the provider handles operations such as patching, scaling and backups |
Containers package the app; Kubernetes runs many containers; serverless hides the servers entirely.
VMs compared with containers
| Virtual machine | Container | |
|---|---|---|
| Includes | Full operating system | App and its dependencies only |
| Size and start time | Larger, slower to start | Smaller, starts in seconds |
| Portability | Tied to an image format | Runs the same on laptop, data center and any cloud |
| Best for | Existing apps, full OS control | Modern apps and microservices |
Business value of Compute Engine
Compute Engine creates and runs VMs on Google's infrastructure.
| Business value | Explanation |
|---|---|
| Fast migration | Existing applications move with little or no change (rehost) |
| Control | Choose the operating system, software and configuration |
| Flexible sizing | Pick predefined or custom machine types, including GPUs for heavy work |
| Pay for what you use | Per-second billing, plus discounts for sustained or committed use |
| Cost savings for flexible work | Spot VMs for batch and fault-tolerant jobs |
| Scalability and availability | Managed instance groups with autoscaling and load balancing across zones |
| Global infrastructure | Run VMs in regions around the world |
Compute Engine is IaaS: you still manage the operating system and software. It is the right choice when you need control or are moving an existing application unchanged.
Spot VMs in practice
Spot VMs are much cheaper than standard VMs, but Google can stop them when it needs the capacity, with only a short warning. Use them for work that can pause and resume: rendering, batch processing, testing, data analysis. Never use them for a database that must stay up.
A worked scenario
A design studio renders animations overnight. It moves its existing render software to Compute Engine unchanged (rehost) and runs the render farm on Spot VMs, because a stopped job can be restarted. Its customer website runs in a managed instance group with autoscaling behind a load balancer, so it adds VMs during launches and removes them afterwards.
How the exam asks about this
- A definition is given and you choose the term (container, microservice, serverless, autoscaling).
- "Lowest-cost compute for batch jobs that can be interrupted." Spot VMs.
- "Run an existing application with full control of the operating system." Compute Engine.
- "Distribute traffic across several servers." Load balancing.
What to remember for the exam
- VMs include a full OS; containers package an app and share the host kernel.
- Microservices are small independent services; a monolith is one unit.
- Serverless: no servers to manage, pay per use.
- Spot VMs: deep discount, can be reclaimed; for fault-tolerant work.
- Kubernetes manages containers; autoscaling adjusts capacity; load balancing spreads traffic.
- Compute Engine gives control and fast migration of existing apps.
Spot VMs are not for workloads that must run without interruption.
Common mistakes
Calling containers lightweight VMs with their own OS. Containers share the host's kernel.
Picking Compute Engine when the team wants no infrastructure work. Serverless fits better.
Confusing autoscaling with load balancing. One changes the number of resources; the other spreads traffic across them.
Check yourself
What is the main tradeoff of Spot VMs?
They are much cheaper, but Google can reclaim them at short notice.
What does Kubernetes do?
It deploys, scales and manages containers across many machines.
When is Compute Engine a good fit?
When an existing application must move quickly with little change or needs full control of the operating system.
Topic 2 of 5 in Modernizing Infrastructure and Applications with Google Cloud
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