M:N Resource Redistribution for High-Availability Failover
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Solution Overview
Problem
Existing high availability systems rely on costly and inefficient 1:1 physical redundant failover configurations, which are difficult to scale and require extensive engineering efforts, especially when hardware components reach end of life, and these systems often need formal hardware/software updates for system expansions.
Innovation Solution
A system dynamically load-balances redistribution elements across a group of computing resources using an M:N working configuration, accessing operational data from a central data store to identify load-balancing opportunities, select redistribution targets, and redeploy elements to maintain high availability without the need for 1:1 physical redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 1:1 physical redundant failover configuration is used, then high availability is achieved, but system cost and device complexity increase significantly
Solution Approach 1:
The patent uses virtualization to create virtual copies of computing resources instead of physical hardware copies. Virtual machine instances can be replicated and migrated across physical hosts, providing redundancy through software-based copying rather than physical duplication. This maintains high availability while reducing hardware complexity.
Solution Approach 2:
The patent replaces mechanical/physical redundant hardware systems with software-based virtualization and orchestration mechanisms. The failover capability is achieved through virtual resource management and software-controlled migration rather than physical hardware switching, thereby reducing device complexity while maintaining reliability.
2Reliability
If 1:1 physical redundant failover configuration is used, then high availability is achieved, but scalability and adaptability deteriorate
Solution Approach 1:
The patent creates a universal pool of computing resources that can serve multiple functions and applications dynamically. The virtualized resource pool can be allocated to different workloads as needed, providing both high availability through redundancy and adaptability through flexible resource assignment. This multi-functional approach eliminates the need for dedicated 1:1 physical redundancy for each application.
Solution Approach 2:
The patent implements dynamic resource allocation and migration capabilities where virtual computing resources can be moved, scaled, and reconfigured in real-time based on system conditions and requirements. This dynamic approach provides high availability through automated failover while simultaneously improving scalability and adaptability to changing demands.
3Reliability
If 1:1 physical redundant failover configuration is used, then failover capability is ensured, but engineering effort and time increase
Solution Approach 1:
The patent implements automated self-service mechanisms for failover detection and execution. The virtualization orchestration system automatically monitors resource health, detects failures, and triggers failover procedures without manual intervention. This automation ensures reliable failover capability while dramatically reducing the engineering time and effort required compared to manual 1:1 physical redundancy configuration.
Solution Approach 2:
The patent performs preliminary configuration of virtual resource pools and failover policies in advance, so that when failures occur, the system can immediately execute pre-planned failover procedures. This preliminary setup reduces both the engineering effort required for configuration and the time needed to respond to actual failures, while maintaining robust failover capability.
4Reliability
If physical hardware redundancy is used, then system reliability is maintained, but resource utilization efficiency decreases
Solution Approach 1:
The patent merges multiple physical computing resources into a unified virtualized pool, allowing resources to be shared and utilized by multiple applications simultaneously. This consolidation maintains system reliability through virtual redundancy while dramatically improving resource utilization efficiency by eliminating idle capacity associated with dedicated 1:1 physical hardware redundancy.
Data Source
AI summary
A system for dynamically load-balancing at least one redistribution element across a group of computing resources that facilitates at least an aspect of an Industrial Execution Process in an M:N working configuration is illustrated. The system is configured to: access from a central or distributed data store, a configuration component operational data and capabilities or characteristics associated with the M:N working configuration; identify a load-balancing opportunity to trigger redistribution of a redistribution element to a redistribution target selected from a redistribution target pool defined by remaining computing resource components associated with the M:N computing resource working configuration; select at least one redistribution target for redeployment; redeploy the at least one redistribution element to the redistribution target; determine redeployment to the at least one selected redistribution target to be a viable redeployment; and execute the Industrial Execution Process utilizing the at least one redistribution element at the selected redistribution target.