Hibernated Node Pool Management for Kubernetes Cluster Scaling
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Solution Overview
Problem
Achieving optimal performance, scalability, and cost-effectiveness in cloud computing environments remains a challenge, particularly in managing and allocating computer resources efficiently.
Innovation Solution
The system manages a pool of hibernated nodes across different resource specifications, resumes a plurality of hibernated nodes with varying resource specifications, and adds these resumed nodes to a running computer cluster, utilizing intelligent cluster scaling protocols and custom software objects for resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual machines are used to allocate computer resources dynamically, then resource allocation flexibility is improved, but system complexity increases
Solution Approach 1:
The patent introduces a node manager as an intermediary component that mediates between the cluster controller and hibernated nodes. The node manager handles node lifecycle events, status monitoring, and coordination of resume operations, thereby managing the complexity of VM-based resource allocation while maintaining flexibility. This intermediary layer abstracts the complexity from the overall system architecture.
2Speed
If hibernated nodes are resumed quickly to meet resource demand, then responsiveness is improved, but energy consumption increases
Solution Approach 1:
The patent implements preliminary actions by maintaining nodes in a hibernated state with pre-configured resource specifications before they are needed. When resource demand arises, these pre-prepared nodes can be resumed quickly without requiring full provisioning from scratch. The system selectively resumes only the necessary number of nodes based on current demand, avoiding unnecessary energy consumption while maintaining responsiveness.
Solution Approach 2:
The system dynamically adjusts the number of active nodes based on real-time resource demand monitoring. The cluster controller continuously assesses resource utilization and triggers selective resumption of hibernated nodes only when thresholds are exceeded. This dynamic approach ensures responsiveness to actual needs while minimizing energy consumption by keeping excess nodes hibernated rather than continuously powered on.
3Adaptability or versatility
If diverse resource specifications are maintained in the node pool, then adaptability to different workloads is improved, but management complexity increases
Solution Approach 1:
The patent segments the node pool into distinct groups based on resource specifications (e.g., compute-optimized, memory-optimized, storage-optimized nodes). Each segment is independently managed and can be selectively resumed based on workload requirements. The node manager tracks and manages each segment separately, which simplifies the overall management complexity while maintaining the ability to provide diverse resource types for different workload demands.
Data Source
AI summary
A system and method for allocating computer resources may include or involve maintaining or managing a pool of hibernated nodes distributed across different resource or instance specifications (such as for example zones or types), resuming a plurality of hibernated nodes, where at least two of the hibernated nodes differ by at least one resource specification, and adding one or more of the resumed nodes to a running computer cluster. Some embodiments may include various intelligent cluster scaling protocols, procedures, and operations, relating, e.g., to expanding the cluster in cases of shortage in computer resources—as well as to node migration, resource or instance optimization, evicting or deleting nodes, and the like. Some example embodiments of the invention may be applied to a Kubernetes cluster environment, and/or may include using custom software objects or custom resource definitions (CRDs) for managing resources, instances, and/or nodes.