Google Compute Engine vs Kubernetes

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Google Compute Engine

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Google Compute Engine vs Kubernetes: What are the differences?

Google Compute Engine and Kubernetes are both powerful tools used in the field of cloud computing. Let's explore the key differences between them.

  1. Scalability and Management: Google Compute Engine allows users to create and manage virtual machines on Google's infrastructure. It provides the flexibility to manually control resource allocation and scaling based on specific needs. On the other hand, Kubernetes is a container orchestration platform that automates the process of deploying, scaling, and managing containers. It offers a more automated and dynamic approach to scaling applications.

  2. Containerization vs Virtualization: Google Compute Engine is primarily focused on virtualization, where users can create and manage virtual machines running various operating systems. It allows users to have full control over the virtual machine's underlying infrastructure. In contrast, Kubernetes is built specifically for containerization. It enables users to deploy and manage containers at scale in a container cluster. Containers offer a lightweight and isolated environment, making them more efficient and faster to deploy than virtual machines.

  3. Service Level Agreement (SLA): Google Compute Engine provides a Service Level Agreement for its virtual machine instances, ensuring a certain level of uptime and reliability. This SLA covers issues related to VM availability and network connectivity. Kubernetes, being a platform for managing containers, does not have its own SLA. The SLA for Kubernetes would depend on the underlying Compute Engine instances or other cloud providers used to run the cluster.

  4. Application Portability and Flexibility: Google Compute Engine allows users to run a wide range of applications and operating systems, giving them greater application portability and flexibility. It supports both Windows and Linux-based virtual machines. On the other hand, Kubernetes provides a platform-agnostic environment for deploying and managing containers. It allows users to run containerized applications across different cloud providers or on-premises infrastructure without any vendor lock-in.

  5. Resource Management: Google Compute Engine empowers users with fine-grained control over resource allocation, allowing them to customize the virtual machine instances to suit their specific needs. It offers flexible options to choose virtual machine types, CPUs, memory, and disk sizes. Kubernetes, on the other hand, abstracts the underlying infrastructure and provides automated resource management. It automatically distributes containers across the cluster, optimizes resource usage, and ensures high availability.

  6. Complexity vs Simplicity: Google Compute Engine provides a more traditional infrastructure as a service (IaaS) model, which gives users more control but also requires more manual management and configuration. Kubernetes, being a container orchestration platform, abstracts many underlying complexities and automates many aspects of application deployment and scaling. While it provides a simpler way to manage containers, it may require a bit of a learning curve to understand its concepts and utilize its full potential.

In summary, Google Compute Engine is primarily focused on virtual machines and offers more control and customization options, while Kubernetes is a container orchestration platform that automates container management and offers greater scalability and application portability.

Decisions about Google Compute Engine and Kubernetes
Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9M views

Our whole DevOps stack consists of the following tools:

  • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
  • Respectively Git as revision control system
  • SourceTree as Git GUI
  • Visual Studio Code as IDE
  • CircleCI for continuous integration (automatize development process)
  • Prettier / TSLint / ESLint as code linter
  • SonarQube as quality gate
  • Docker as container management (incl. Docker Compose for multi-container application management)
  • VirtualBox for operating system simulation tests
  • Kubernetes as cluster management for docker containers
  • Heroku for deploying in test environments
  • nginx as web server (preferably used as facade server in production environment)
  • SSLMate (using OpenSSL) for certificate management
  • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
  • PostgreSQL as preferred database system
  • Redis as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

  • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
  • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
  • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
  • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
  • Scalability: All-in-one framework for distributed systems.
  • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
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Stephen Fox
Artificial Intelligence Fellow · | 2 upvotes · 187.4K views

GCE is much more user friendly than EC2, though Amazon has come a very long way since the early days (pre-2010's). This can be seen in how easy it is to edit the storage attached to an instance in GCE: it's under the instance details and is edited inline. In AWS you have to click the instance > click the storage block device (new screen) > click the edit option (new modal) > resize the volume > confirm (new model) then wait a very long time. Google's is nearly instant.

  • In both cases, the instance much be shut down.

There also the preference between "user burden-of-security" and automatic security: AWS goes for the former, GCE the latter.

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Pros of Google Compute Engine
Pros of Kubernetes
  • 87
    Backed by google
  • 79
    Easy to scale
  • 75
    High-performance virtual machines
  • 57
    Performance
  • 52
    Fast and easy provisioning
  • 15
    Load balancing
  • 12
    Compliance and security
  • 9
    Kubernetes
  • 8
    GitHub Integration
  • 7
    Consistency
  • 3
    Good documentation
  • 3
    One Click Setup Options
  • 3
    Free $300 credit (12 months)
  • 2
    Ease of Use and GitHub support
  • 2
    Great integration and product support
  • 2
    Escort
  • 1
    Integration with mobile notification services
  • 1
    Easy Snapshot and Backup feature
  • 1
    Low cost
  • 1
    Support many OS
  • 1
    Very Reliable
  • 1
    Nice UI
  • 164
    Leading docker container management solution
  • 128
    Simple and powerful
  • 106
    Open source
  • 76
    Backed by google
  • 58
    The right abstractions
  • 25
    Scale services
  • 20
    Replication controller
  • 11
    Permission managment
  • 9
    Supports autoscaling
  • 8
    Cheap
  • 8
    Simple
  • 6
    Self-healing
  • 5
    No cloud platform lock-in
  • 5
    Promotes modern/good infrascture practice
  • 5
    Open, powerful, stable
  • 5
    Reliable
  • 4
    Scalable
  • 4
    Quick cloud setup
  • 3
    Cloud Agnostic
  • 3
    Captain of Container Ship
  • 3
    A self healing environment with rich metadata
  • 3
    Runs on azure
  • 3
    Backed by Red Hat
  • 3
    Custom and extensibility
  • 2
    Sfg
  • 2
    Gke
  • 2
    Everything of CaaS
  • 2
    Golang
  • 2
    Easy setup
  • 2
    Expandable

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Cons of Google Compute Engine
Cons of Kubernetes
    Be the first to leave a con
    • 16
      Steep learning curve
    • 15
      Poor workflow for development
    • 8
      Orchestrates only infrastructure
    • 4
      High resource requirements for on-prem clusters
    • 2
      Too heavy for simple systems
    • 1
      Additional vendor lock-in (Docker)
    • 1
      More moving parts to secure
    • 1
      Additional Technology Overhead

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    What are some alternatives to Google Compute Engine and Kubernetes?
    Google App Engine
    Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
    DigitalOcean
    We take the complexities out of cloud hosting by offering blazing fast, on-demand SSD cloud servers, straightforward pricing, a simple API, and an easy-to-use control panel.
    Google Cloud Platform
    It helps you build what's next with secure infrastructure, developer tools, APIs, data analytics and machine learning. It is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube.
    Amazon EC2
    It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
    Microsoft Azure
    Azure is an open and flexible cloud platform that enables you to quickly build, deploy and manage applications across a global network of Microsoft-managed datacenters. You can build applications using any language, tool or framework. And you can integrate your public cloud applications with your existing IT environment.
    See all alternatives