Cloud Service Metric Instrumentation for Anonymized Peer Rankings
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Solution Overview
Problem
Businesses face challenges in obtaining timely and actionable peer rankings due to the latency in collecting and publishing metrics, which hinders corrective actions, and existing methods fail to provide rapid and continuous anonymized extramural business rankings.
Innovation Solution
A system and method that utilizes instrumentation to monitor cloud service metrics, compares them across tenants, and generates anonymized extramural business rankings in real-time or near-real-time, enabling on-demand reporting and correlation of metrics to application parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If businesses use traditional quarterly metric publication methods, then data accuracy and completeness are improved, but the timeliness of peer rankings deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously collecting and preprocessing metric data from cloud service tenants in real-time through instrumentation, so that when peer rankings are requested, the data is already prepared and available immediately, eliminating the quarterly delay while maintaining accuracy through continuous validation
Solution Approach 2:
The system implements continuous metric collection and processing operations through cloud service instrumentation, maintaining an ongoing stream of updated data that enables real-time peer rankings without the interruptions and delays inherent in periodic quarterly reporting cycles
2Loss of time
If businesses collect and publish metrics frequently (daily basis), then the timeliness of peer rankings is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The cloud service vendor acts as an intermediary that centralizes the complex tasks of metric collection, normalization, and processing. Individual tenants simply provide data through standard cloud service instrumentation, while the vendor's platform handles the complexity of continuous aggregation, validation, and ranking computation across multiple tenants
Solution Approach 2:
The cloud service platform provides a universal multi-functional solution that simultaneously handles metric collection from diverse sources, data normalization across different tenants and applications, continuous processing, storage, and ranking generation. This consolidated platform approach eliminates the need for each business to build and maintain separate complex real-time analytics infrastructure
3Loss of information
If cloud service vendors collect granular measurements from multiple tenants, then the availability of peer comparison data is improved, but the privacy protection requirement creates a constraint
Solution Approach 1:
The system extracts and removes identifying information and sensitive details from the collected metric data, retaining only the normalized performance metrics needed for peer comparison. This extraction process separates the useful ranking information from the privacy-sensitive identifiers, enabling peer comparisons without exposing tenant-specific identifying data
Solution Approach 2:
The system transforms the raw metric data by changing its parameters through normalization and aggregation, converting detailed tenant-specific measurements into standardized, anonymized performance indicators. This parameter transformation maintains the comparative value of the data while eliminating privacy risks associated with raw granular measurements
Data Source
AI summary
A method, system, and computer program product for computing cloud services. A method commences upon invocation of instrumentation configured to monitor a plurality of applications running in a cloud environment. The instruments are configured to capture a first set of metrics and a second set of metrics of respective cloud service tenants. The captured metrics are compared and the comparisons are used to perform a ranking. The rankings of the compared metrics are then used to compare cloud service tenants (e.g., a respective first cloud service tenant is compared with respect to a second cloud service tenant). The ranking is based at least in part on the compared metrics. The rankings can be fairly compared by selecting the first set of metrics with respect to the second set of metrics where both sets of metrics pertain to a common domain, and/or a common application, and/or a common application feature.