Electron microscopy
 
PythonML
Google Kubernetes Engine (GKE)
- Python Automation and Machine Learning for ICs -
- An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao -
Python Automation and Machine Learning for ICs                                                           http://www.globalsino.com/ICs/        


Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

=================================================================================

Google Kubernetes Engine (GKE) is a managed environment provided by Google Cloud for deploying, managing, and scaling containerized applications using Google infrastructure. GKE is built on Kubernetes, an open-source container orchestration platform designed to automate the deployment, scaling, and operations of application containers across clusters of hosts. GKE offers:

  • Automated Management
    • Cluster Management: GKE manages the underlying infrastructure of your containerized applications automatically, handling tasks like maintenance, scaling, and updates. This includes upgrading the Kubernetes software on clusters and applying security patches.
    • Node Pools: Customize clusters with node pools that specify the types of virtual machines and quantities used. Node pools allow for the configuration of different machine types and sizes for specific workloads.
  • Integrated Ecosystem
    • Integration with Google Cloud Services: GKE offers native integration with other Google Cloud services, such as Cloud Storage, BigQuery, and Cloud Pub/Sub. This integration helps to streamline the development process and enhances functionality with services like monitoring, logging, and continuous deployment.
    • Marketplace: Access to Google Cloud Marketplace that offers ready-to-go development stacks, solutions, and services optimized to run on GKE.
  • Security and Compliance
    • Security: Implements several security features like encrypted data storage and network policies that control traffic flow at the IP address or port level. It also supports private clusters, which restricts access to cluster master endpoints by making them accessible only from internal IP addresses.
    • Compliance: GKE is compliant with major standards and certifications, ensuring that deployments adhere to regulatory requirements.
  • Scalability and Flexibility
    • Automatic Scaling: GKE can automatically adjust the number of nodes in the cloud based on the load with its Cluster Autoscaler feature.
    • Load Balancing: Supports automatic load balancing for deployed applications, helping to distribute traffic efficiently across a cluster to maintain optimum performance and reduce downtime.
  • Developer and Operational Productivity
    • Kubernetes APIs: Offers full access to Kubernetes APIs, enabling developers to use standard Kubernetes tools and commands within the GKE environment.
    • Logging and Monitoring: Integrates with Cloud Monitoring and Cloud Logging, providing insights into the health, performance, and availability of applications and clusters.
  • Cost Management
    • Sustained Use Discounts and Committed Use Discounts: Google Cloud's pricing model can offer cost savings through long-term commitments and sustained usage.
  • Multi-Cloud and Hybrid Cloud Capabilities
    • Anthos Integration: GKE is a central part of Anthos, Google's platform for managing applications in a multi-cloud environment, including on-premises. This allows for the consistent development and operation of applications across various deployment environments.

===========================================

         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         

 

 

 

 

 



















































 

 

 

 

 

=================================================================================