Intelligent Failover Migration Scoring Destination Clusters
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Solution Overview
Problem
High availability systems face challenges in optimizing failovers across multiple stacks, as existing methods lack intelligence in selecting the best destination cluster based on historical data, leading to suboptimal downtime and reliability.
Innovation Solution
A computer-implemented method that monitors failover data, scores prior failovers, and generates instructions to migrate applications to the most suitable destination cluster based on historical performance metrics, including migration time, uptime, and external data factors like best practices and customer preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional failover methods are used without intelligence in selecting destination cluster, then the system maintains simplicity in operation, but the downtime increases and reliability decreases
Solution Approach 1:
The system automatically monitors failover data, scores destination clusters based on historical performance, and selects the optimal destination without human intervention. The scoring mechanism evaluates multiple factors including migration time, uptime, and external data factors, enabling the system to self-optimize failover operations while maintaining simplicity for users.
Solution Approach 2:
The system continuously monitors failover data from multiple high availability stacks and uses this feedback to score and rank destination clusters. This feedback loop allows the system to learn from past failovers and improve future selections, thereby increasing reliability while keeping the operational interface simple.
2Productivity
If historical failover data is analyzed and scored to select optimal destination, then the reliability and performance improve, but the system complexity increases
Solution Approach 1:
The system segments the failover decision-making process into distinct components: data collection from multiple stacks, scoring of destination clusters based on historical performance, and selection of the optimal destination. This segmentation allows each component to be managed independently, reducing overall system complexity while improving failover efficiency.
Solution Approach 2:
The system changes the parameters used for failover selection from simple static criteria to dynamic scored parameters based on historical data. By evaluating multiple parameters such as migration time, uptime, and external factors, the system improves productivity while managing complexity through structured parameter evaluation.
3Measurement precision
If multiple high availability stacks are monitored and scored, then the selection accuracy improves, but the time required for failover decision increases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and scoring destination clusters before failover events occur. Historical performance data is collected and analyzed in advance, so when a failover is needed, the system can quickly retrieve pre-computed scores and make accurate decisions without time-consuming analysis during the actual failover event.
Data Source
AI summary
A computer-implemented method includes: monitoring, by a computing device, failover data from one or more high availability stacks, wherein the failover data includes data associated with prior failovers associated with the one or more high availability stacks; scoring, by the computing device, a plurality of prior failovers identified in the failover data; generating, by the computing device, a failover instruction for implementing a failover based on the scoring; and causing, by the computing device, and based on the failover instruction, an application to migrate from a target cluster to a destination cluster.