Cloud Disaster Recovery Metering Validation
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Solution Overview
Problem
There is a need to validate the metering of disaster recovery services in cloud environments to ensure accuracy in billing and resource usage measurements, particularly in hybrid clouds where real-time validation and testing are challenging due to complex pricing models and dynamic changes in consumption patterns.
Innovation Solution
A system is implemented that compares measured usage values from a secondary cloud with expected values based on prior configurations, allowing customers to validate the accuracy of disaster recovery service metering by provisioning a test cloud with identical configurations and comparing usage metrics over specified durations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a test cloud is provisioned with identical configurations to validate metering accuracy, then measurement precision is improved, but device complexity and time consumption increase
Solution Approach 1:
The patent creates a copy cloud that replicates the configuration, resources, and disaster recovery settings of the original cloud. This copy is used specifically for validation purposes to verify metering accuracy without affecting production systems. The copy cloud mirrors the same virtual machines, storage resources, and disaster recovery configurations to ensure comparable metering measurements.
Solution Approach 2:
The validation process is performed preliminarily by provisioning the copy cloud before actual disaster recovery operations. Metering measurements are collected and compared in advance to verify accuracy of billing and resource usage calculations before they impact real business operations.
2Reliability
If metering validation is performed in hybrid cloud environments with complex pricing models, then reliability of billing is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements feedback mechanisms where metering measurements from the copy cloud are continuously compared against expected values based on configured pricing models. Discrepancies are automatically detected and reported, providing feedback on billing accuracy. The system collects usage data, applies pricing rules, and generates validation reports that feedback to stakeholders about metering reliability.
Solution Approach 2:
The validation framework is designed to be universal and work across different cloud environments (public, private, hybrid) and various pricing models. It handles multiple resource types (compute, storage, network) and pricing structures (on-demand, reserved, spot instances) through a unified validation approach that doesn't require environment-specific customization.
3Productivity
If real-time metering validation is implemented in dynamic cloud environments, then productivity of billing processes is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The validation system operates continuously by maintaining the copy cloud in a running state and continuously collecting metering measurements. Rather than periodic snapshots, the system maintains continuous monitoring of resource usage, disaster recovery operations, and associated costs, enabling real-time validation of billing accuracy in dynamic cloud environments.
Data Source
AI summary
An aspect of the present disclosure facilitates validating metering of a disaster recovery service used in clouds. In one embodiment, a system receives a request to validate metering of usage of a disaster recovery service (DRS) in a first cloud. The system collects from a metering service of the DRS, measured values representing the actual usage of the DRS in a second cloud and then compares the measured values with corresponding expected values representing expected usage of the DRS in the second cloud. The system sends a response to the request based on a result of the comparing. In one embodiment, the request is received from a tenant (customer/owner) owning the first cloud.