Workload Assignment Balancing in High Availability Systems
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Solution Overview
Problem
Designing high availability systems (HA systems) is challenging due to the complexity of manually configuring workload assignments and ensuring load balancing, especially in the presence of failures, where conventional algorithms like round robin can lead to unbalanced workload distribution and subsequent performance degradation.
Innovation Solution
A method and configuration generator that distribute backup assignments and balance active workload assignments among service units, using algorithms to ensure even load distribution before and after failures, by treating workload assignment as a Constraints Solving Problem (CSP) and considering the redundancy model used in the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional algorithms like round robin are used for workload assignment, then the configuration process is simple, but the workload distribution becomes unbalanced after failures leading to performance degradation
Solution Approach 1:
The system performs preliminary analysis of service unit failures and pre-calculates optimal workload redistribution plans. When a failure occurs, the pre-computed redistribution strategy is immediately applied, avoiding the need for complex real-time calculations and ensuring balanced workload distribution without performance degradation.
Solution Approach 2:
The workload assignment system dynamically adjusts the distribution strategy based on the current system state and failure conditions. Rather than using a static round-robin approach, the system continuously monitors service unit status and recalculates optimal assignments to maintain balance under varying conditions.
2Adaptability or versatility
If manual configuration is used for workload assignments, then flexibility in design is achieved, but the process becomes tedious and error-prone
Solution Approach 1:
The system automatically generates workload assignment configurations by analyzing service unit characteristics, failure scenarios, and redundancy models. The configuration generator performs self-service by computing optimal assignments without requiring manual intervention, thereby eliminating errors while preserving design flexibility through configurable parameters.
Solution Approach 2:
The system allows flexible configuration through parameter adjustment rather than manual structural design. By changing parameters such as redundancy model selection, service unit weights, and failure probability distributions, the system adapts to different design requirements automatically, reducing manual effort while maintaining versatility.
3Reliability
If backup assignments are concentrated on specific service units, then redundancy is ensured, but those units become overloaded during normal operation
Solution Approach 1:
The system assigns different backup responsibilities to different service units based on their local characteristics, capacity, and current workload. Rather than concentrating backup roles on specific units, each service unit is assigned backup responsibilities proportionate to its capabilities, ensuring redundancy while maintaining local load balance and preventing any single unit from becoming overloaded.
Data Source
AI summary
Among other things, embodiments described herein enable systems, e.g., Availability Management Forum (AMF) systems, having service units to operate with balanced loads both before and after the failure of one of the service units. A configuration can be generated which provides for distributed backup roles and balanced active loads. When a failure of a service unit occurs, the active loads previously handled by that service unit are substantially evenly picked up as active loads by remaining service units.