Cloud Resiliency Scoring for Hardware Failure Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In cloud-based service-oriented architectures, ensuring high availability and disaster recovery while maintaining resiliency in the face of hardware and software failures is challenging, particularly due to the aging of physical drivers and components, which can lead to increased failure probabilities and disruptions.
Innovation Solution
A method that involves receiving time-date data sets and machine logic-based rules to determine a resiliency value for server computers, which is then used to recommend changes to hardware, firmware, high availability policies, and disaster recovery plans, thereby enhancing the system's ability to handle failures and maintain service integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high availability policies and disaster recovery plans are implemented, then service availability is improved, but system complexity increases
Solution Approach 1:
The system dynamically adjusts resiliency parameters (such as monitoring intensity, backup frequency, and failover thresholds) based on the calculated resiliency score. When hardware components age or performance degrades, the system modifies operational parameters to maintain service availability without requiring complete system redesign or permanent complex configurations.
2Reliability
If proactive hardware replacement and policy adjustments are made, then resiliency is improved, but resource consumption increases
Solution Approach 1:
The system performs preliminary assessments by calculating resiliency scores based on hardware age, performance metrics, and operational data. By identifying components that are likely to fail soon, the system enables proactive replacement or adjustment before actual failures occur, preventing service disruptions and reducing the need for emergency resource allocation.
Solution Approach 2:
The system continuously monitors hardware performance and updates the resiliency score based on feedback from operational data. This closed-loop approach allows the system to adjust resource allocation dynamically - increasing resources when resiliency is low and reducing resources when resiliency is adequate - thereby optimizing the balance between reliability and resource consumption.
3Measurement precision
If monitoring and analysis of hardware age and performance are intensified, then failure detection is improved, but operational overhead increases
Solution Approach 1:
The resiliency score calculation system serves multiple functions simultaneously: it assesses hardware health, predicts failures, guides replacement decisions, and informs policy adjustments. By consolidating these functions into a single unified metric, the system reduces operational overhead compared to maintaining separate monitoring systems for each function.
Data Source
AI summary
Evaluating a plurality of computers hosting a cloud platform for effectiveness at operating through operational failures with minimal or no degradation to operations by identifying vulnerabilities in hardware, firmware, software and operational policy/plan aspects of the plurality of computers and managing the identified vulnerabilities by modifying hardware, firmware, software, and operational policy/plan aspects of the plurality of computers and the hosted cloud platform to improve effectiveness at operating through operational failures with minimal or no degradation to operations.