Machine Learning Service Level Agreement Generation for Data Backup
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Solution Overview
Problem
The process of identifying and implementing a service level agreement for data backup is often dependent on application-specific expertise, leading to increased cost and effort, and existing systems lack efficient automation for this process.
Innovation Solution
A data management system (DMS) employs a machine learning model, such as a K-means clustering algorithm, to automatically identify and implement a service level agreement for data backup by analyzing workload metadata and recommending configurations based on historical data, allowing for periodic assessment and updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert input is used to identify and implement service level agreement, then reliability of service level agreement configuration is improved, but device complexity and cost increase
Solution Approach 1:
The system performs self-service by automatically generating service level agreement configurations using machine learning models. The DMS analyzes workload metadata and historical data to autonomously determine appropriate service level agreements without requiring external expert intervention, thereby maintaining reliability while reducing complexity and cost.
Solution Approach 2:
The patent replaces the mechanical system of expert human analysis with an automated machine learning-based system. The ML model processes workload metadata and historical service level agreement data to generate configurations, substituting human expertise with computational algorithms that reduce system complexity while maintaining or improving configuration accuracy.
2Reliability
If expert input is used to identify and implement service level agreement, then service level agreement quality is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and analyzing workload metadata and historical data before service level agreement implementation is needed. The machine learning model is trained in advance on historical service level agreement configurations, enabling rapid generation of quality configurations without requiring time-consuming expert analysis when deployment is required.
Solution Approach 2:
The patent replaces the time-consuming mechanical process of expert analysis with automated machine learning algorithms that can rapidly process workload metadata and generate service level agreement configurations, significantly reducing the time required while maintaining or improving quality.
3Adaptability or versatility
If manual identification of service level agreement is used, then adaptability to specific requirements is improved, but productivity decreases
Solution Approach 1:
The system achieves adaptability through parameter changes by adjusting service level agreement configurations based on workload metadata parameters and historical patterns. The machine learning model dynamically modifies service level agreement parameters to match specific workload requirements while maintaining high productivity through automated processing, eliminating the need for manual customization.
4Productivity
If automation is introduced for service level agreement identification, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The system implements feedback mechanisms by training the machine learning model on historical service level agreement configurations and their outcomes. The model learns from past performance data, continuously improving its ability to generate accurate configurations while maintaining high productivity through automated processing. Feedback loops ensure that automation does not compromise precision.
Solution Approach 2:
The system performs preliminary training and validation actions before deployment. The machine learning model is pre-trained on extensive historical data and validated against known service level agreement requirements, ensuring that automation maintains high measurement precision while achieving improved productivity through rapid configuration generation.
Data Source
AI summary
Methods, systems, and devices for data management are described. A data management system (DMS) may receive a request to backup data from a source data storage environment to a target data storage environment. The DMS may then input first workload metadata associated with backing up the data from the source data storage environment to the target data storage environment into a machine learning model that is trained using second workload metadata associated with a set of workloads managed by the data management system. The DMS may generate, via the machine learning model and in response to the request, one or more service level agreement configurations for backing up the data. Then, the DMS may perform the backup of the data from the source data storage environment to the target data storage environment in accordance with at least one of the one or more service level agreement configurations.