Boundaryless HA Control for Dynamic M:N Load Redistribution
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Solution Overview
Problem
Existing high availability systems rely on expensive and difficult-to-scale 1:1 physical redundant failover configurations, requiring extensive engineering efforts and formal hardware/software definitions, especially when expanding production, and struggle with managing outdated hardware components nearing end of life.
Innovation Solution
A system dynamically load-balances redistribution elements across a group of computing resources by monitoring operational states and availability metrics, identifying load-balancing opportunities, and redistributing elements to maintain high availability without the need for 1:1 physical redundancy, using a M:N working configuration and auto-remediation to ensure system reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 1:1 physical redundant failover configuration is used, then system reliability is improved, but hardware cost and device complexity increase significantly
Solution Approach 1:
The patent merges multiple redundant resources into a shared pool where N computing resources support M applications. Instead of dedicating one standby resource per application (1:1), multiple resources are combined into a common pool that dynamically serves multiple applications, reducing total hardware while maintaining reliability through load redistribution upon failure.
Solution Approach 2:
Computing resources in the N-resource pool are designed to be universal and multi-functional, capable of supporting any of the M applications rather than being dedicated to a single application. This universality allows any resource to take over any application's workload, eliminating the need for application-specific redundant hardware.
2Reliability
If 1:1 physical redundant failover configuration is used, then system reliability is improved, but scalability deteriorates due to extensive engineering efforts
Solution Approach 1:
The system implements dynamic load balancing and automatic redistribution mechanisms that adapt in real-time to failures and capacity changes. When resources or applications are added or removed, the system dynamically recalculates and redistributes workloads without requiring manual reconfiguration or formal engineering definitions, enabling easy scaling.
Solution Approach 2:
The patent changes the fundamental parameter from fixed 1:1 pairing to flexible M:N ratios. The system monitors resource utilization and dynamically adjusts the distribution of M applications across N resources based on current capacity and demand, allowing the M:N ratio itself to be modified as the system scales without structural redesign.
3Quantity of substance
If dynamic load-balancing redistribution is implemented, then hardware requirements are reduced, but system complexity in managing distributed resources increases
Solution Approach 1:
The system implements self-service automation where the load balancing and failover mechanisms operate autonomously without manual intervention. The control system automatically detects failures, calculates optimal redistribution, and executes resource reallocation based on real-time monitoring of resource capacity and application performance, eliminating the need for complex manual management procedures.
Solution Approach 2:
The patent incorporates continuous feedback loops that monitor resource utilization, application performance, and system state. This feedback drives automatic load balancing decisions and failover actions, with the system constantly adjusting resource distribution based on real-time conditions, thereby managing complexity through closed-loop control rather than static configurations.
4Productivity
If M:N working configuration is used instead of 1:1 redundancy, then resource utilization is optimized, but difficulty of detecting and measuring system state increases
Solution Approach 1:
The patent introduces an intermediary control system that acts as a mediator between the M applications and N resources. This intermediary maintains a centralized view of system state, resource capacity, and application performance, simplifying the detection and measurement of operational conditions by consolidating information from the complex M:N relationships into a unified management interface.
Data Source
AI summary
In a Boundaryless Control High Availability (“BCHA”) system (e.g., industrial control system) comprising multiple computing resources (or computational engines) running on multiple machines, technology for computing in real time the overall system availability based upon the capabilities/characteristics of the available computing resources, applications to execute and the distribution of the applications across those resources is disclosed. In some embodiments, the disclosed technology can dynamically manage, coordinate recommend certain actions to system operators to maintain availability of the overall system at a desired level. High Availability features may be implemented across a variety of different computing resources distributed across various aspects of a BCHA system and/or computing resources. Two example implementations of BCHA systems described involve an M:N working configuration and M:N+R working configuration.