Self-Orchestrating Containers with Integrated Intelligence
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Solution Overview
Problem
Kubernetes' complexity can limit productivity, especially for simpler applications or when running smaller numbers of applications, and transitioning to it can be costly and cumbersome.
Innovation Solution
Self-orchestrating containers with integrated intelligence, including an in-memory state component for detecting container instances and a quorum synchronization component for coordinating activities, manage applications across multiple clusters without relying on external third-party applications, eliminating the need for Kubernetes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Kubernetes is used to manage containerized workloads, then resource scheduling and allocation are improved, but system complexity increases
Solution Approach 1:
The patent extracts the orchestration functionality from external third-party applications like Kubernetes and embeds it directly into the containers themselves. Each container includes an integrated intelligence component that enables autonomous decision-making about resource allocation, scheduling, and coordination, eliminating the need for separate control plane components.
Solution Approach 2:
Containers are designed to be self-orchestrating through integrated intelligence components. The containers autonomously detect their own state, make decisions about resource allocation, coordinate with other containers, and manage their own lifecycle without requiring external management systems. This self-service capability reduces system complexity while maintaining scheduling efficiency.
2Productivity
If Kubernetes components (controller and scheduler) are deployed, then workload management is improved, but operational complexity increases
Solution Approach 1:
The patent removes the separate controller and scheduler components from the system and integrates their functionality directly into each container. The integrated intelligence component performs workload management tasks locally within the container, eliminating the need for centralized control plane operations and simplifying operational complexity.
Solution Approach 2:
Instead of having centralized control components managing all workloads, the patent segments management responsibilities by embedding intelligence directly in each container. Each container independently manages its own workload tasks, making the system more operationally simple while maintaining effective workload management capabilities.
3Productivity
If third-party applications are used for container orchestration, then scheduling capabilities are improved, but cost increases
Solution Approach 1:
The patent extracts the need for external third-party orchestration applications by embedding scheduling and coordination capabilities directly into the container image itself. This eliminates the cost of licensing and transitioning to services like Kubernetes, while maintaining comprehensive scheduling capabilities through the integrated intelligence component.
Solution Approach 2:
Containers perform their own scheduling and coordination tasks through integrated intelligence, eliminating the need to pay for external orchestration services. The self-service capability includes autonomous resource allocation, topology awareness, and coordination with other containers, all performed without third-party intervention.
4Extent of automation
If containers use integrated intelligence with in-memory state, then autonomous decision-making is improved, but memory resource consumption increases
Solution Approach 1:
The patent implements a quorum-based synchronization mechanism where containers only maintain and process state information up to a certain threshold level. The in-memory state component stores only the essential topology and coordination information needed for autonomous decisions, avoiding the excessive memory consumption that would result from storing complete system state in every container.
Solution Approach 2:
The patent segments state management by maintaining localized in-memory state within each container for topology-aware decisions, rather than requiring each container to hold complete system state. This segmentation approach enables autonomous decision-making while reducing individual container memory consumption through selective state caching.
Data Source
AI summary
A plurality of containers can be configured for running applications associated to at least one node of a distributed computing environments. The containers of the plurality of containers includes integrated intelligence that provides an in memory state component that detects how container instances are running. A quorum synchronization component of the integrated intelligence can coordinate the activities of the containers. A first container can be initiated for running a first node application. The memory state component can determine if a topology exists in the plurality of containers that is running an existing application matching the first node application. The quorum synchronization component of the integrated intelligence can coordinate running of the first node application with the first container with the existing application.