Computer Environment Risk Scoring for Proactive Service Disruption Prevention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying I/O workloads that may cause future disruptions in computer environments are inefficient and time-consuming, often requiring collaboration among multiple divisions with their own expertise, leading to potential disruptions until the workloads are identified and remediated.
Innovation Solution
A software module generates a risk score for alerts using a risk algorithm considering various factors, amplifies alerts meeting a predefined criterion, and automatically determines remediations to prevent future disruptions by executing query scripts on identified storage resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple divisions collaborate to analyze I/O workloads, then identification accuracy is improved, but time consumption increases
Solution Approach 1:
The patent segments the complex workload analysis task into distinct functional modules: a scoring module that evaluates individual workload characteristics against predefined criteria, and an amplification module that aggregates scores to identify high-risk workloads. This segmentation allows parallel processing of multiple workload parameters simultaneously, achieving comprehensive analysis without requiring sequential collaboration across multiple divisions, thereby reducing time consumption while maintaining identification accuracy.
Solution Approach 2:
The patent introduces an intermediary amplification module that acts as a bridge between raw workload data and final identification results. This module aggregates scores from multiple evaluation criteria and applies threshold-based filtering to identify workloads requiring attention. The intermediary processing layer enables automated decision-making that would otherwise require human collaboration, significantly reducing analysis time while preserving the comprehensive evaluation approach.
2Reliability
If conventional monitoring methods are used, then system stability is maintained, but proactive prevention capability is lost
Solution Approach 1:
The patent implements preliminary action by proactively identifying workloads with high risk scores before they cause actual disruptions. The scoring module continuously evaluates workload characteristics against predefined criteria, and the amplification module identifies workloads exceeding threshold values, enabling remediation actions to be taken in advance. This preliminary identification capability transforms reactive monitoring into proactive prevention, maintaining system stability while significantly improving productivity by preventing disruptions before they occur.
Solution Approach 2:
The patent establishes a feedback loop where identified high-risk workloads trigger automated or manual remediation processes, and the results feed back into the scoring system to refine future identifications. This continuous feedback mechanism enhances the system's ability to maintain stability by learning from past events and improving its proactive prevention capability over time, addressing both reliability and productivity concerns.
3Measurement precision
If manual analysis processes are used, then expertise utilization is improved, but efficiency decreases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform workload analysis and identification without requiring manual intervention from multiple expert divisions. The scoring module autonomously evaluates workload characteristics against predefined criteria, and the amplification module automatically identifies high-risk workloads based on aggregated scores. This automated self-service approach eliminates the need for time-consuming manual collaboration while preserving the comprehensive evaluation methodology, thereby significantly improving efficiency without sacrificing expertise utilization.
Solution Approach 2:
The patent transforms the analysis process from manual qualitative assessment to automated quantitative evaluation by changing the parameters from expert judgment to measurable scoring criteria. The system evaluates workloads based on objective parameters such as I/O intensity, resource utilization, and temporal patterns, converting them into numerical scores that can be automatically processed and compared. This parameter transformation enables efficient automated processing while maintaining the depth of analysis previously requiring human expertise.
Data Source
AI summary
Techniques are provided for proactive analysis of a computer environment to prevent a disruption in services. Specifically, a risk score may be generated for each of a plurality of different alerts. If the risk score generated for a particular alert meets a predefined criterion, the alert may be amplified to determine a remediation for the computer environment. Specifically, an alert with a risk score that meets a predefined criterion may be amplified to determine one or more remediations that can be implemented to proactively prevent a future and potential disruption in services in a computer environment. For example, a service process that has a greatest utilization at a particular storage resource, e.g., storage array, may be identified. One or more query scripts may then be executed within the database using the particular storage resource to identify a remediation that can be implemented to prevent a potential future disruption in services.