ML-Based SLA Configuration for Adaptive Data Backup
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Solution Overview
Problem
The process of identifying a correct service level agreement for data backup in a data management system is often dependent on application-specific expertise, leading to increased cost and effort, and existing methods do not efficiently automate this process.
Innovation Solution
A data management system uses a machine learning model, such as K-means clustering, to identify the most frequently used service level agreement configurations based on workload attributes, and periodically assesses and updates these agreements to adapt to changes in workload requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert-based methods are used to identify service level agreements, then reliability of service level agreement selection is improved, but device complexity and cost increase
Solution Approach 1:
The system performs self-service by automatically generating service level agreements through machine learning models without requiring external expert intervention. The ML model analyzes workload attributes and autonomously determines appropriate SLC configurations, eliminating the need for human experts while maintaining reliability.
Solution Approach 2:
The patent replaces the mechanical system of expert-based manual configuration with an automated machine learning system. The ML model substitutes human expertise by processing workload data and generating SLC configurations algorithmically, thereby reducing system complexity while preserving selection accuracy.
2Reliability
If expert-based methods are used to identify service level agreements, then reliability of service level agreement selection is improved, but loss of time and effort increase
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on historical workload data and service level agreement configurations. This pre-processing enables the model to quickly generate accurate SLC recommendations without requiring time-consuming expert analysis for each new workload.
Solution Approach 2:
The patent replaces the time-consuming mechanical process of expert-based configuration with automated machine learning inference. The ML model rapidly processes workload attributes and generates SLC configurations in minutes rather than hours or days, significantly reducing time loss while maintaining reliability.
3Productivity
If automated methods are used to identify service level agreements, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system performs preliminary action by extensively training the machine learning model on large datasets of historical workload characteristics and corresponding optimal service level agreements. This pre-training ensures that when the automated system operates, it has already learned precise patterns and relationships, maintaining high configuration accuracy while achieving automation efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors the performance and outcomes of generated service level agreements. This feedback is used to refine and retrain the machine learning model, progressively improving manufacturing precision while maintaining productivity through automated operation.
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.