Cloud Metering Service Carry-Forward Mechanism for Billing Accuracy
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Solution Overview
Problem
Cloud computing systems face challenges in implementing fine-grained usage tracking and billing, preserving historical data granularity, and detecting and resolving billing errors due to issues with metering and billing capabilities, particularly in cloud architectures that offer infrastructure-as-a-service (IaaS) with consumption-based billing.
Innovation Solution
A cloud computing system that includes a metering service using a carry-forward mechanism to reduce network traffic for billing, detects anomalies in service events through a reconciliation protocol, and modifies rating data based on replayed service events to ensure accurate billing and error resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If service events are transmitted continuously for metering and billing, then billing accuracy is improved, but network traffic increases
Solution Approach 1:
The system transforms continuous service events into periodic rating data at scheduled intervals. The metering service collects service events and aggregates them into periodic rating periods, transmitting data only at the end of each rating period rather than continuously. This periodic aggregation maintains billing accuracy while significantly reducing network traffic volume.
Solution Approach 2:
The metering service performs preliminary aggregation and validation of service events before transmission. Service events are collected, validated for completeness, and aggregated into periodic rating data in advance of billing cycles. This preliminary processing ensures data accuracy is maintained while reducing the frequency and volume of network transmissions.
2Quantity of substance
If service events are aggregated into periodic rating data, then network traffic is reduced, but detection of billing errors becomes more difficult
Solution Approach 1:
The reconciliation service implements feedback mechanisms by comparing periodic rating data against service event logs and billing records. The system continuously monitors for discrepancies between expected and actual ratings, automatically detecting billing errors through this feedback loop. Error metrics are tracked and reported back to system operators for correction.
Solution Approach 2:
The reconciliation service acts as an intermediary layer between the metering service and billing system. It validates periodic rating data against service events, detects anomalies and billing errors, and facilitates error resolution. This intermediary layer maintains error detection capability while working with aggregated periodic data.
3Quantity of substance
If a carry-forward mechanism is used to remember last-known rating state, then network traffic is reduced further, but handling of lost or duplicate events becomes more complex
Solution Approach 1:
The reconciliation service serves as an intermediary that manages the complexity of carry-forward mechanisms. It tracks service events, identifies lost or duplicate events by comparing event sequences, and corrects rating data accordingly. This intermediary handles the complex logic of carry-forward error detection and correction, keeping the overall system manageable despite the increased complexity.
4Measurement precision
If fine-grained tracking of usage is implemented, then billing accuracy is improved, but data storage requirements increase
Solution Approach 1:
The system segments usage data into discrete service events and aggregates them into periodic rating periods. Fine-grained service events are tracked at the event level with full granularity, but for storage and billing purposes, they are segmented into periodic rating data. This segmentation maintains detailed tracking capability while reducing overall storage requirements through structured aggregation.
Solution Approach 2:
The metering service implements periodic aggregation of fine-grained service events into rating periods. Detailed usage data is captured at the event level but stored in aggregated periodic formats. This periodic structure preserves necessary granularity for billing accuracy while significantly reducing storage requirements compared to storing every individual event indefinitely.
Data Source
AI summary
A cloud computing system includes services that provide virtual cloud computing resources. The service publishes service events that are transformed into periodic rating data as per centralized, pluggable, service-specific business rules. The cloud computing system includes a metering service that reduces network traffic for billing using a carry-forward mechanism that remembers a last-known rating state. Anomalies in the last-known state, such as, from lost or duplicate service events, can be detected via a reconciliation protocol between the metering service and cloud computing services.