Cluster Data Resource Risk Assessment Method
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In big data scenarios, improper use of cluster hardware resources can lead to use risks, potentially causing serious service accidents. Similarly, improper use of cluster data resources, comprising data storage and computation resources, also poses risks that need to be assessed in advance to avoid service accidents.
Innovation Solution
A method and apparatus for assessing risks caused by improper use of cluster data resources. This involves obtaining assessment dimensions for risk assessment, identifying high-risk diagnosis indicators for storage and computation resources, counting trigger quantities for these indicators, and subsequently assessing risks based on these counts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cluster data resources are monitored and assessed using multiple dimensions and indicators, then risk detection capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the risk assessment system into distinct dimensions (storage dimension and computation dimension) with specific indicators for each. The storage dimension includes indicators like storage usage rate and storage capacity, while the computation dimension includes indicators like computation resource usage rate. This segmentation allows comprehensive risk monitoring without overwhelming system complexity by organizing assessment parameters in a structured hierarchy.
2Measurement precision
If high-risk diagnosis indicators are established with specific thresholds and rules, then risk assessment accuracy is improved, but implementation complexity increases
Solution Approach 1:
The patent establishes specific parameter thresholds and rules for risk assessment, such as setting the storage usage rate threshold at 80% and computation resource usage rate threshold at 70%. These parameter changes transform abstract risk concepts into measurable, actionable indicators with clear decision boundaries, improving assessment accuracy while maintaining implementability through quantifiable metrics.
3Reliability
If comprehensive risk assessment is performed on both storage and computation resources, then service accident prevention is improved, but computational overhead increases
Solution Approach 1:
The patent extracts and focuses on the most critical risk indicators from the broader set of possible monitoring parameters. By identifying and monitoring only the key indicators (storage usage rate, storage capacity, computation resource usage rate) that directly correlate with service accidents, the system achieves comprehensive risk assessment while minimizing computational overhead by excluding less relevant parameters.
Data Source
AI summary
A method and an apparatus for assessing a risk caused by improper use of a cluster data resource, an electronic device and a storage medium are provided. A high-risk diagnosis indicator in a storage assessment dimension and a high-risk diagnosis indicator in a computation assessment dimension are obtained. The amount of high-risk diagnosis indicators in the storage assessment dimension that are satisfied in the use process of the data storage resource and the amount of high-risk diagnosis indicators in the computation assessment dimension that are satisfied in the use process of the data computation resource are counted, and are recorded as high-risk indicator trigger quantities. Risk on the data storage resource is assessed based on the high-risk indicator trigger quantity in the storage assessment dimension, and risk on the data computation resource is assessed based on the high-risk indicator trigger quantity in the computation assessment dimension.