Container Orchestration Updates Using Git-Backed DAG Scheduling
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Solution Overview
Problem
Existing container orchestration platforms face limitations in environments requiring specific ordering and timing of updates, especially in automotive environments, leading to reduced capabilities, efficiency, reliability, and predictability due to the use of eventual-consistency algorithms.
Innovation Solution
Utilizing a version control system repository with hooks to provide a backing store for cluster data, incorporating directed-acyclic graphs (DAGs) to control the ordering and timing of multi-stage updates, ensuring deterministic scheduling and synchronization across multiple datacenters or cloud providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If eventual-consistency algorithms are used in container orchestration platforms, then the system can handle dynamic environments with flexibility, but the ability to enforce specific ordering and timing of updates is lost
Solution Approach 1:
The patent segments the update process into multiple stages with explicit ordering requirements. Each update operation is divided into discrete phases that must execute in a specific sequence, allowing the system to maintain flexibility while enforcing deterministic update ordering through structured segmentation of the orchestration workflow.
Solution Approach 2:
The patent implements preliminary actions by requiring pre-flight checks and validation steps before updates are applied. The system performs predetermined actions such as verifying update readiness, checking dependencies, and preparing target states before executing the actual update, ensuring that ordering and timing constraints are satisfied in advance.
2Reliability
If multi-stage updates with deterministic scheduling are implemented, then update ordering and timing control is improved, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-stage update framework that can handle various types of updates (configurations, deployments, scaling operations) through a common deterministic scheduling mechanism. This universal approach reduces complexity by providing a single standardized process that works across different update scenarios rather than requiring separate mechanisms for each update type.
Solution Approach 2:
The patent introduces an intermediary scheduling layer that mediates between the desired update state and the actual system state. This intermediary component manages the complexity of deterministic scheduling by acting as a buffer that coordinates multi-stage updates, handles dependencies, and ensures ordering constraints are met without requiring complex changes to the underlying orchestration infrastructure.
3Reliability
If version control system repository is used as backing store, then data consistency and version tracking are improved, but access and update operations become more complex
Solution Approach 1:
The patent applies copying by creating and maintaining a local cache or copy of the repository state within the container orchestration platform. This allows the system to access versioned data and maintain consistency without requiring frequent direct accesses to the version control repository, thereby reducing the complexity of repository operations while preserving data consistency and version tracking capabilities.
Data Source
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AI summary
A change to a repository of a version control system is identified. A processing device may provide update data to a container orchestration platform in response to identification of the change to the repository of the version control system. The update data may be associated with a multi-stage update on a component of the container orchestration platform. The update data may indicate scheduling information associated with each stage of the multi-stage update. The repository may be updated based on completion data received from the container orchestration platform in response to performing at least a portion of the multi-stage update based on the update data.