Cloud Framework for Real-Time Customer Experience Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for measuring customer experience in multi-tenant cloud computing environments face challenges in providing real-time data without compromising accuracy, leading to unacceptable computational costs and delays due to the complexity of processing large volumes of transaction logs.
Innovation Solution
A cloud computing framework that uses full datasets to generate accurately computed profiles 'in the background,' allowing for real-time or near real-time computation of customer experience indicators, which are then applied uniformly across tenants and system entities for effective remedial action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full datasets are processed to accurately measure customer experience, then measurement precision is improved, but computational cost and processing time increase
Solution Approach 1:
The system pre-computes and stores customer experience profiles in the background using full datasets before real-time monitoring is needed. These pre-computed profiles are then applied uniformly across tenants during real-time operations, eliminating the need to process complete datasets at the moment of measurement while maintaining full data accuracy.
Solution Approach 2:
The customer experience measurement system is divided into two independent components: (1) background profile generation that processes complete datasets offline to create accurate reference profiles, and (2) real-time monitoring that applies these pre-generated profiles uniformly across multiple tenants. This segmentation allows each component to operate independently at its optimal performance level.
2Measurement precision
If complete transaction logs are analyzed for each tenant, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Complete transaction log analysis is performed in advance during background profile generation, creating comprehensive customer experience profiles that capture all nuances of tenant interactions. These pre-analyzed profiles are then applied uniformly during real-time monitoring, eliminating the need for complex real-time log processing while maintaining measurement precision.
Solution Approach 2:
Instead of processing original transaction logs during real-time monitoring, the system creates and applies copied customer experience profiles that were generated from complete log analysis. These profiles serve as simplified representations that retain all necessary measurement information without requiring access to the complex original data structures.
3Productivity
If real-time customer experience monitoring is implemented across all tenants, then productivity is improved, but computational cost increases
Solution Approach 1:
All computationally intensive customer experience profile generation is performed in advance during background operations using available computing resources. During real-time monitoring, the system simply applies these pre-computed profiles uniformly across tenants, requiring minimal computational resources while maintaining full monitoring capability and enabling immediate remedial actions.
Data Source
AI summary
System and methods are described for deriving normalized infrastructure metrics to represent customer experience of a cloud computing system, continuously evaluating a profile for a tenant of the cloud computing system and determining a customer experience indicator for the tenant in real-time or near real-time, without losing accuracy, based at least in part on the infrastructure metrics and the profile.