Anonymous Indicator Aggregation in Multi-Instance Computing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In multi-instance computing environments, customers cannot compare and analyze indicator values on a global scale due to design constraints that isolate instances, preventing the exchange of performance metrics between different customer instances.
Innovation Solution
A system and method for anonymously aggregating indicator values from multiple customer instances, filtering out outliers, and generating global indicator values based on filtered aggregations, which are then transmitted back to relevant customer instances for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If instances are isolated to maintain security and privacy, then customer data protection is improved, but global performance comparison capability deteriorates
Solution Approach 1:
A central instance acts as an intermediary between customer instances, collecting indicator values anonymously and generating global indicator values without exposing raw customer data. This mediator enables global performance comparison while maintaining instance isolation and data protection.
Solution Approach 2:
The system creates anonymous copies of indicator values from customer instances, processes these copies to generate global metrics, and returns aggregated results. The original customer data remains protected while enabling global analysis through anonymized replicas.
2Loss of information
If indicator values are aggregated from multiple instances, then global performance analysis is improved, but system complexity deteriorates
Solution Approach 1:
The central instance performs multiple functions: collecting indicator values from various customer instances, filtering outliers, generating global indicator values, and distributing results. This multi-functional approach consolidates complexity into a single component rather than requiring distributed aggregation logic across all instances.
Solution Approach 2:
The system transforms raw indicator values from customer instances into global indicator values by changing the aggregation parameters and calculation methods. Different aggregation functions (averages, percentiles, etc.) are applied to transform individual metrics into meaningful global performance indicators.
3Loss of information
If all indicator values are collected and processed, then comprehensive analysis is improved, but processing time deteriorates
Solution Approach 1:
The system extracts and removes outlier values from the collected indicator data before generating global indicator values. By taking out extreme values that would skew results, the processing becomes more efficient and the global metrics more representative, reducing the need for complex handling of anomalous data.
Solution Approach 2:
The system collects indicator values from a subset of customer instances rather than requiring all instances to participate in every aggregation cycle. This partial action approach reduces processing time while still providing meaningful global performance insights through statistical sampling.
Data Source
AI summary
Indicator values are anonymously aggregated in a multi-instance computing environment. Aggregations of indicator values are received from customer instances in a multi-instance computing environment. At least one of the aggregations of indicator values is generated by a respective customer instance of the customer instances based on indicator values generated by the respective customer instance. The aggregations of indicator values are filtered to produce filtered aggregations, and the filtered aggregations are aggregated according to characteristics associated with at least some of the customer instances to generate global indicator values. Each global indicator value is generated from one or more of the filtered aggregations that are greater than a minimum threshold. One or more of the global indicator values may then be transmitted to a customer instance of the customer instances having a characteristic corresponding to those global indicator values.