Differentiated Workload Telemetry for Hybrid Cloud Licensing
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Solution Overview
Problem
Current workload telemetry systems fail to effectively generate differentiated data for licensing models in hybrid and multi-cloud environments, particularly in managing disparate systems and legacy applications, leading to inefficiencies in resource usage monitoring and dynamic licensing.
Innovation Solution
A system that uses event emitters to collect and correlate telemetry data from cloud services, performing real-time analysis and reporting to create a dynamic licensing model by instrumenting cloud resources for self-reporting and accurate usage metering across hybrid and multi-cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional telemetry systems are used in hybrid and multi-cloud environments, then basic monitoring is achieved, but differentiated workload data for licensing models cannot be generated
Solution Approach 1:
The patent segments the telemetry system into multiple specialized components: event emitters for data collection, correlation engines for relationship analysis, domain context labelers for categorization, and workload type classifiers for differentiation. This segmentation enables precise measurement of different workload types while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces intermediary components including a global event handler that coordinates between multiple event emitters, a correlation engine that mediates relationship analysis, and a workload classifier that acts as an intermediary between raw telemetry data and licensing decisions. These intermediaries enable precise workload differentiation without requiring direct complex interactions between all system components.
2Measurement precision
If real-time correlation analysis is performed on telemetry data, then accurate workload monitoring is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-defining workload type categories and correlation rules before telemetry data arrives. Domain context labelers pre-establish categorization schemes, and the correlation engine pre-configures relationship patterns to analyze. This preparation enables rapid real-time processing without requiring complex analysis to be constructed from scratch for each data point.
Solution Approach 2:
The patent changes parameters by transforming raw telemetry data into standardized workload metrics through domain context labeling and correlation scoring. The system converts diverse cloud resource data into unified workload type classifications with associated confidence scores, enabling efficient comparison and decision-making without processing every raw data point in detail.
3Loss of information
If comprehensive telemetry data collection is implemented across all cloud resources, then complete visibility is achieved, but data volume and storage requirements increase
Solution Approach 1:
The patent extracts only the essential telemetry data elements needed for workload classification and licensing decisions. Event emitters are configured to collect specific metrics relevant to workload types (CPU, memory, storage, network usage patterns) while filtering out redundant information. This extraction maintains complete visibility into workload characteristics without storing unnecessary data volume.
Solution Approach 2:
The patent applies partial action by collecting comprehensive telemetry data only for resources and time periods relevant to licensing decisions. The system monitors workload patterns continuously but focuses detailed analysis on resources that impact licensing metrics, using sampling and aggregation for less critical data. This approach ensures data completeness for decision-making while reducing overall storage requirements.
Data Source
AI summary
In an approach for generating differentiated workload telemetry data, a processor corresponds one or more services with a workload related telemetry generating an event emitter. A processor performs a correlation analysis of corresponding relationship and connection among connected resources and current traffic into and out of the one or more services. A processor labels domain context for each telemetry event. A processor communicates each telemetry event to a global event handler. A processor performs a cross-correlation in real-time of telemetry data with the global event handler. A processor updates a real-time differentiated workload report.