Cohort-Based Cloud Performance Estimation
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Solution Overview
Problem
Current cloud-based service providers offer performance estimates based solely on internet bandwidth, failing to account for other critical factors that affect application performance, such as computing environment characteristics, leading to inaccurate predictions for entities considering a switch from local to cloud-based solutions.
Innovation Solution
The system groups entities with similar performance metrics into cohorts based on comprehensive computing environment characteristics, including internet bandwidth, distance to the cloud-server, internal resources, and user demographics, allowing for more accurate performance metric estimation and issue diagnosis by comparing new entities to established cohorts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If performance estimates are based solely on internet bandwidth, then the estimation process is simple, but the accuracy of performance predictions deteriorates
Solution Approach 1:
The system segments entities into cohorts based on multiple computing environment characteristics (bandwidth, distance to cloud-server, internal resources, user demographics). This segmentation allows performance estimates to be tailored to specific cohorts rather than using a single blanket estimate, thereby improving accuracy without requiring completely new estimation methods
Solution Approach 2:
The system transitions from one-dimensional bandwidth-based estimation to multi-dimensional cohort-based estimation by incorporating additional characteristics such as distance to cloud-server, internal computing resources, and user demographics. This dimensional expansion improves prediction accuracy while maintaining manageable complexity through systematic cohort grouping
2Measurement precision
If multiple computing environment characteristics are considered for cohort grouping, then performance estimate accuracy improves, but system complexity increases
Solution Approach 1:
The system creates universal cohorts that can serve multiple entities with similar characteristics. Once a cohort is established through initial data collection, it can be reused to provide performance estimates for multiple new entities, reducing the overall complexity of the system while maintaining high estimation accuracy
Solution Approach 2:
The system performs preliminary data collection and cohort establishment before providing performance estimates to new entities. By pre-grouping entities into cohorts based on their characteristics, the system avoids the need to collect and process all possible data for every new entity, thereby reducing ongoing system complexity
3Reliability
If cohorts are used to diagnose performance issues, then the ability to identify root causes improves, but the time required for diagnosis increases
Solution Approach 1:
The system uses cohort performance data as feedback to quickly diagnose individual entity issues. By comparing an entity's performance against its cohort's established performance profile, the system can rapidly identify whether performance problems are entity-specific or cohort-wide, significantly reducing diagnosis time while maintaining high accuracy
Data Source
AI summary
Systems and methods for estimating a performance metric based on cohorts are provided. One or more performance metrics with respect to an application are monitored for entities over time. Entities with similar performance metrics are grouped into cohorts. Characteristics of a computing environment for each entity are determined. The characteristics of the entities in each cohort are used to define the characteristics of the cohort. Later, when a new entity inquires about the performance metrics that the entity could expect, the characteristics of the computing environment of the new entity are determined and are used to determine the cohort that the new entity belongs to. The average performance metrics of the entities in the determined cohort can be return to the new entity as the expected performance metrics.