Modular Cluster Operators for Hybrid Cloud Disaster Recovery
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Solution Overview
Problem
Current methods for deploying software clusters in hybrid cloud environments lack a holistic disaster recovery solution, require manual intervention, deep technical knowledge, and lack modular components for efficient resource management, leading to complex cloud resource utilization and unclear boundary definitions.
Innovation Solution
A unified method using control software to replicate software clusters by generating software operators based on analysis of existing clusters, determining configuration requirements, and creating templates for deployment in new environments, allowing for modular and flexible deployment across different cloud platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate solutions are used for different cloud environments, then each environment can be addressed with specific capabilities, but the overall system complexity increases and management becomes more difficult
Solution Approach 1:
The patent implements a universal disaster recovery platform that can operate across multiple cloud environments (AWS, Azure, GCP, on-premises) through a single unified architecture. The control software cluster with operators provides multi-functional capability to manage container workloads across different cloud providers, eliminating the need for separate vendor-specific solutions while maintaining environment-specific adaptability through configurable templates and operators
2Reliability
If manual intervention is required for migration activities, then complex technical decisions can be made with human expertise, but the deployment time and operational complexity increase
Solution Approach 1:
The patent implements automated self-service capabilities where the control software cluster with operators automatically performs disaster recovery operations including workload assessment, resource provisioning, and container deployment. The system uses automated operators to monitor cluster health, detect failures, and execute recovery procedures without manual intervention, reducing deployment time while maintaining reliability through programmed decision logic
3Reliability
If deep technical knowledge is required to implement disaster recovery solutions, then complex configurations can be optimized, but the ease of operation decreases and requires highly skilled personnel
Solution Approach 1:
The patent introduces an intermediary control software layer with operators that mediates between the user and the complex Kubernetes cluster management. The operators act as intelligent intermediaries that handle complex technical configurations, resource orchestration, and cloud-provider-specific details, allowing users with minimal technical knowledge to achieve effective disaster recovery through simplified interfaces and automated decision-making
4Reliability
If more components are used to manage control plane resiliency, then the system can maintain better availability, but the resource overhead and management burden increase
Solution Approach 1:
The patent segments the control plane management into modular operators that can be independently deployed and managed. Each operator handles specific aspects of cluster resiliency (e.g., node monitoring, pod scheduling, resource management), allowing the system to maintain high availability through distributed functionality while reducing overall resource overhead compared to monolithic control plane implementations. The segmentation enables selective activation of resiliency features based on specific operational needs
Data Source
AI summary
Replicating a software cluster which includes control software and containers in another computing environment can include software operators which can be generated relating to modular functionalities of a control software cluster. The software operators are derived from analyzing the control software cluster. The software operators further being derived from analyzing code of the control software cluster including the containers, and from analyzing software configurations for the control software cluster. The generating of the software operators can include, at least in part, determining software configuration requirements for the control software cluster. The generating of the software operators including at least in part, creating a template for the control software cluster based on the determined software configuration requirements and the analysis of the another software computing environment. All or a portion of the software operators of the control software cluster can be replicated in the another software computing environment.