Cross-Cluster Container Capacity Sharing for Dynamic Node Reallocation
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Solution Overview
Problem
Existing container orchestration systems face inefficiencies in resource utilization and cost management due to limited compute capacity within cluster boundaries, leading to waste and increased expenses when clusters are not fully utilized.
Innovation Solution
Implement a mechanism for cross-cluster capacity scaling by broadcasting and mapping capacity requirements and availability across multiple clusters, enabling deallocation and reallocation of nodes between clusters based on supply and demand, using predictive modeling and rules-based scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If container orchestration systems are limited to single-cluster boundaries, then cluster management is simpler, but resource utilization is inefficient and costs increase
Solution Approach 1:
The patent merges multiple independent clusters into a unified federated system, allowing resources across cluster boundaries to be shared and coordinated. The federated cluster manager combines capacity information from multiple clusters and enables cross-cluster node allocation, transforming isolated cluster resources into a pooled shared resource system that improves utilization efficiency.
Solution Approach 2:
The system creates a universal capacity pool that serves multiple clusters simultaneously. Nodes can be dynamically allocated to different clusters based on demand, and the federated manager provides multi-functional capacity optimization across the entire federated system, allowing the same physical resources to serve multiple logical clusters as needed.
2Device complexity
If clusters operate independently without cross-cluster sharing, then billing and management are simpler, but capacity waste increases and costs rise
Solution Approach 1:
The system implements feedback mechanisms where the federated cluster manager continuously monitors capacity utilization across all clusters, receives capacity information from individual clusters, and dynamically adjusts node allocations based on real-time demand signals. This feedback loop enables automatic optimization of resource distribution while maintaining billing integrity through usage tracking.
Solution Approach 2:
The federated cluster manager acts as an intermediary layer between individual clusters and the resource pool. It mediates capacity sharing, handles cross-cluster node allocation, and maintains billing records, thereby enabling complex multi-cluster resource optimization without requiring direct complex interactions between all clusters while preserving billing clarity.
3Stability of the object's composition
If nodes are statically allocated within clusters, then resource allocation is more stable, but adaptability to changing demand decreases
Solution Approach 1:
The system transitions from static node allocation to dynamic cross-cluster resource sharing. Nodes can be automatically reallocated between clusters based on changing demand conditions, with the federated manager continuously adjusting allocations to match current workload requirements while maintaining stable service delivery through coordinated management.
Solution Approach 2:
The system performs preliminary capacity planning and node provisioning at the federated level before demand fluctuations occur. By pre-configuring the capacity pool and establishing allocation policies in advance, the system can quickly respond to demand changes by activating pre-prepared resources from the pool rather than needing to provision from scratch during peak demand.
Data Source
AI summary
A method, computer program product, and computer system are provided for container cross-cluster capacity scaling. The method includes broadcasting local capacity information of capacity availability or capacity requirement for the local cluster and receiving broadcasts from each of one or more other clusters providing capacity information including capacity availability or capacity requirements. The method may map the received capacity information with the local capacity information and may determine a suitable cross-cluster capacity sharing when a capacity requirement of the local cluster maps to a capacity availability of another cluster or when a capacity availability of the local cluster maps to a capacity requirement of another cluster. The method may coordinate the deallocation of a node from the cluster having the capacity availability and reallocation of the node to the cluster having the capacity requirement.