Kubernetes Microservice Restore Policy
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Solution Overview
Problem
Cloud native applications with microservices architectures face challenges in efficiently restoring critical components after failures, as existing methods lack granularity and automation in determining the importance of microservices and their persistent data, leading to unnecessary resource usage and load during restore processes.
Innovation Solution
Implementing an automated backup and restore policy system that utilizes service meshes like Istio to detect microservice relationships, rank their importance, and create customized backup and restore plans, allowing for selective and prioritized restoration of only necessary microservices and data, reducing storage usage and resource load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all microservices are restored after a failure, then system reliability is improved, but resource usage and restore time increase significantly
Solution Approach 1:
The patent segments the microservice restoration process by identifying and separating critical microservices from non-critical ones. The system discovers microservice dependencies and ranks them by importance, then restores only the critical segment first. This segmentation allows selective restoration rather than restoring all microservices, thereby reducing restore time while maintaining essential system functionality.
Solution Approach 2:
The patent extracts and identifies the essential critical components from the entire microservice system. By using dependency discovery mechanisms, the system extracts the subset of microservices that are truly necessary for core functionality and restores them first, leaving non-critical microservices for later restoration. This extraction principle directly reduces restore time while preserving system reliability.
2Reliability
If all persistency microservices are backed up, then data security is improved, but storage usage increases
Solution Approach 1:
The patent applies local quality by assigning different backup priorities to different microservices based on their criticality. Critical microservices receive higher backup priority and are backed up more frequently or with higher redundancy, while non-critical microservices have lower backup priority. This differentiated approach ensures data security for essential services while reducing overall storage consumption compared to uniform backup of all microservices.
Solution Approach 2:
The patent implements partial action by backing up only the critical subset of microservices rather than all microservices. The system identifies which microservices are essential for core functionality and applies backup operations selectively to those. This partial backup approach maintains adequate data security for critical operations while significantly reducing storage usage compared to comprehensive backup of the entire system.
3Productivity
If microservice dependencies are analyzed to determine restore order, then restore efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically discover and analyze microservice dependencies without requiring manual configuration. The dependency discovery mechanism autonomously maps the microservice architecture, identifies critical paths, and determines restore orders based on discovered relationships. This automation improves restore efficiency while managing system complexity through self-configuration rather than manual intervention.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting restore priorities based on discovered dependency relationships. The system changes the restore order parameter from a static or random sequence to a dynamically determined sequence based on criticality analysis. This parameter adaptation improves restore efficiency by ensuring critical microservices are restored first, while the complexity is managed through algorithmic determination rather than manual configuration.
Data Source
AI summary
One example method includes discovering an application topology, discovering relationships between microservices that are elements of the application topology, ranking the microservices, identifying one or more of the microservices as a persistency microservice, selecting one or more persistency microservices for backup, and defining a backup policy based on the microservice relationships, the microservice rankings, and the selected persistent microservices. A backup operation is then performed that includes backing up persistent data and/or persistent metadata generated and/or modified by one or more of the persistency microservices.