On-Demand Metering via Modular Data Collection and Processing
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Solution Overview
Problem
Current metering systems in on-demand service environments lack accuracy, dynamism, thoroughness, and flexibility, making it difficult to accurately bill clients and optimize resource allocation, as they struggle to discern individual client usage and record detailed transactions across various resources.
Innovation Solution
A method utilizing a web services interface and processing engine that collects and processes usage data through a data gathering agent, storing records in a database with a Record Processing Program and applying configuration data to generate metrics for accounting, auditing, and billing, while maintaining a flexible format to accommodate various types of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional metering system is used to track resource consumption, then the system can provide basic monitoring capabilities, but it lacks accuracy in discerning individual client usage and recording detailed transactions
Solution Approach 1:
The metering system is segmented into distinct functional modules: data collection agents deployed at individual client systems, network interceptors for packet capture, database storage systems for detailed transaction records, and processing engines for generating metrics. This segmentation enables precise tracking of individual client resource consumption while maintaining system manageability through modular architecture.
Solution Approach 2:
The patent introduces intermediary components including network interceptors that capture packets between clients and servers, and data collection agents that act as mediators between the monitoring system and target applications. These intermediaries enable accurate metering of resource usage without requiring direct modification of client systems or core application logic.
2Adaptability or versatility
If a comprehensive metering system is implemented to track all resource consumption details, then accurate billing and optimization become possible, but the system becomes less flexible when new resource types are added
Solution Approach 1:
The metering system employs a universal data collection agent architecture that can monitor multiple resource types (CPU, memory, disk, network, application-specific resources) through a common framework. The system uses standardized data structures and configurable parameters that allow new resource types to be added by simply configuring the existing agents rather than developing new monitoring infrastructure.
Solution Approach 2:
The system enables adaptability to new resources through parameter configuration rather than structural modification. Data collection agents use configurable parameters to identify and measure different resource types, allowing the system to adapt to new resources by changing measurement parameters and thresholds without altering the core system architecture.
3Measurement precision
If detailed transaction records are retained for auditing and billing purposes, then measurement accuracy is improved, but data storage and transmission requirements increase
Solution Approach 1:
The system extracts and retains only the essential metering data elements needed for billing and auditing purposes, separating critical usage information from transient monitoring data. Database storage is optimized to retain aggregated metrics and summary statistics while maintaining detailed transaction records only when necessary for dispute resolution or compliance requirements.
Solution Approach 2:
The system implements selective data retention where detailed transaction records are maintained for a baseline period sufficient for most billing disputes, after which data is aggregated into summary metrics. This partial retention strategy maintains measurement precision for the required audit period while reducing long-term storage requirements through progressive data aggregation.
4Speed
If real-time monitoring is implemented to provide immediate observations, then the system can detect usage patterns quickly, but it lacks the counting and accumulation capabilities needed for accurate billing
Solution Approach 1:
The system performs preliminary data collection and validation at the source through data collection agents that continuously monitor resource usage and buffer measurements before transmission. This preliminary action ensures data integrity and completeness before aggregation, enabling both real-time detection capabilities and accurate cumulative billing metrics without requiring separate monitoring systems.
Solution Approach 2:
The metering system maintains continuous data collection and measurement operations through always-running data collection agents that accumulate resource usage metrics without interruption. This continuous operation ensures neither real-time detection nor billing accuracy is compromised, as the system simultaneously provides immediate usage visibility and maintains precise cumulative totals for billing purposes.
Data Source
AI summary
A method for metering on-demand service (ODS) resource consumption by a user comprising a web services interface, a processing engine, and configuration data. A data gathering agent in the ODS stores the usage data in records, combines the records into packets, and transmits the packets to the web services interface. The web services interface passes the packets onto the processing engine, which stores the records in the packet into a database. The generalized format of the record is critical because it allows the metrics to be stored in the record table with the usage data. When a record contains an instruction to close a UOW, the processing engine applies the configuration data comprising composition rules and record selection rules to the database. The record selection rules select the desired records from the database. The composition rules aggregate, normalize, and compose the selected records to produce a metric.