Data Recovery Objective Modeling for Storage Networks
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Solution Overview
Problem
Determining the appropriate hardware and software configurations for storage network environments to achieve a specified recovery point objective (RPO) is time-consuming and prone to errors, as existing methods rely on manual rules and estimations, leading to inefficiencies in data replication and potential data loss during failures or disasters.
Innovation Solution
A data recovery objective model is defined to automatically determine an estimated RPO based on client data ingest rates, effective throughput, network bandwidth, and available processing resources, which can recommend necessary computing resource upgrades to ensure adequate replication and failover capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual rules of thumb and human estimations are used to determine hardware and software configurations, then the process is simple to implement, but it is time-consuming and error-prone
Solution Approach 1:
The system performs self-service by automatically determining hardware and software configurations through a data transfer utility that calculates RPO metrics based on collected statistical utilization data, eliminating the need for manual human estimation and rules of thumb
Solution Approach 2:
The patent replaces the mechanical human decision-making process with an automated computational system that uses a data transfer utility to calculate RPO metrics and determine appropriate configurations based on statistical data and mathematical models
2Reliability
If a smaller RPO time is specified to reduce data loss, then data recovery capability is improved, but less time is available to finish data transfers before starting subsequent transfers
Solution Approach 1:
The system dynamically adjusts transfer parameters and timing based on the specified RPO metric and collected statistical utilization data, optimizing the balance between data recovery capability and transfer throughput by calculating appropriate transfer windows and scheduling
Solution Approach 2:
The patent changes key parameters such as transfer timing, data selection criteria, and resource allocation based on the RPO metric and statistical utilization data to achieve both high reliability and productivity
3Measurement precision
If automated determination of RPO metrics is implemented, then accuracy and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent introduces a data transfer utility as an intermediary component that collects statistical utilization data and applies mathematical models to calculate RPO metrics, providing accurate measurements while encapsulating the complexity within a manageable software layer
Solution Approach 2:
The system uses statistical utilization data as a copy or representation of actual system behavior to model and predict RPO metrics without requiring complex real-time simulation of all system operations
Data Source
AI summary
One or more techniques and/or systems are provided for data recovery objective modeling. For example, a data recovery objective model may be defined for a storage network environment. The data recovery objective model may be defined based upon a client data ingest rate corresponding to a data change rate by one or more clients of data stored by a first storage controller. The data recovery objective model may be defined based upon an effective throughput of a data transfer utility for replicating modified data from first storage of the first storage controller to second storage of a second storage controller. Statistical utilization data may be collected from the storage network environment, and may be evaluated using the data recovery objective model to determine a data recovery objective metric. If the data recovery objective metric does not satisfy a client specified objective, then a computing resource recommendation may be provided.