History and Motivation

2000s: Traditional Deployment Era

During the 2000s, we experienced what is known as the "Traditional Deployment Era". This period was characterized by:

• On-premises Deployments: Companies managed their own data centers or used colocation services.
• Teams of Sysadmins: Dedicated teams of system administrators were responsible for provisioning and managing fleets of servers, which was often a labor-intensive and immature process.
• Bare Metal Servers: Applications ran directly on physical servers.
• Monolithic Architecture: The prevalent architectural style was monolithic, where applications were built as single, indivisible units.
• Homegrown Monitoring Tools: Monitoring and managing applications required custom-built tools due to the lack of standardized solutions.

2010s: Virtualized Deployment Era

The 2010s marked the transition to the "Virtualized Deployment Era". Key developments during this time included:

• Cloud Computing: The advent of cloud computing allowed Virtual Machines (VMs) to be created and destroyed in minutes, providing greater flexibility and scalability.
• Configuration Management Tools: Tools like Puppet and Chef became popular for managing infrastructure as code, simplifying the configuration and management of large-scale deployments.
• Manual Bin-Packing: Applications were manually allocated to VMs, optimizing resource usage but still requiring significant manual effort.
• Improved Tooling: The emergence of better tooling made it practical to manage a larger number of applications and cloud resources.
• Challenges with Scale: Despite the improvements, managing large numbers of cloud resources remained a significant challenge.

2020s: Container Deployment Era

In the 2020s, we entered the "Container Deployment Era", which brought about transformative changes in how workloads are managed:

• Workload Orchestrators: Tools like Kubernetes enabled treating clusters of machines as a single resource, simplifying management and scaling.
• Standard Interfaces and Utilities: These orchestrators provided a range of utilities and interfaces to handle:
  • Efficient Scheduling: Optimally distributing workloads across instances.
  • Health Checks: Monitoring the health and status of applications.
  • Service Discovery: Automating the detection of service locations within the cluster.
  • Configuration Management: Standardizing the way configurations are managed and applied.
  • Autoscaling: Automatically adjusting the number of running instances based on demand.
  • Persistent Storage: Managing storage that persists beyond the lifecycle of individual containers.
  • Networking: Ensuring reliable and scalable networking between services.

Kubernetes History

For a deeper dive into the history and development of Kubernetes, you can check out the two-part documentary by Honeypot on YouTube:

• Kubernetes: The Documentary [PART 1].
• Kubernetes: The Documentary [PART 2]