Storage Context Replication Policies Using Association Rules
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Solution Overview
Problem
Distributed storage systems and object storage systems lack proper support for automatic data replication, leading to inefficiencies and user frustration due to insufficient understanding and lack of experience in managing replication policies.
Innovation Solution
A system utilizing an association rule-based policy suggestion engine that employs machine learning to automatically determine replication policies based on historical data and user feedback, allowing for efficient and context-aware data replication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual replication policy management is used, then users have control over replication settings, but it leads to user frustration and inefficiency due to lack of expertise and time-consuming configuration
Solution Approach 1:
The system automatically generates replication policies by analyzing storage context parameters without requiring manual user configuration. The patent implements self-service through automated policy generation that uses historical data and machine learning models to create appropriate replication policies based on the storage system's characteristics, eliminating the need for users to manually configure complex replication settings
Solution Approach 2:
The system incorporates user feedback loops where users can review, accept, or modify automatically generated policies. This feedback mechanism allows the system to learn from user preferences and improve policy generation over time, combining automation with user control to resolve the contradiction between ease of operation and policy effectiveness
2Productivity
If automated replication policy generation is implemented, then processing efficiency improves, but system complexity increases due to machine learning models and historical data requirements
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical replication data and storage context parameters before automated policy generation is needed. This pre-processing of data establishes a foundation that enables efficient automated policy creation without requiring complex real-time analysis, thereby improving productivity while managing system complexity through advance preparation
Solution Approach 2:
The patent introduces an association rule-based policy suggestion engine as an intermediary between raw historical data and final replication policies. This intermediary component simplifies the complexity by using association rules to extract meaningful patterns from historical data, making the automated policy generation process more manageable and interpretable while maintaining high productivity
3Measurement precision
If context-aware replication policies are used, then replication accuracy improves, but the extent of automation required increases system resource consumption
Solution Approach 1:
The system applies partial automation by using association rules to identify only the most relevant storage context parameters for policy generation, rather than analyzing all possible parameters. This selective approach maintains replication policy accuracy by focusing on critical factors while reducing computational resource consumption by avoiding exhaustive analysis of every parameter
Data Source
AI summary
Facilitating auto enable replication for detected storage context in advanced communication networks is described. An example method includes determining, by a system comprising at least one processor, that data stored in a source system is to be replicated to a target system. The method also includes, based on historical data related to a storage context, determining, by the system, a replication policy for a replication of the data to the target system. The replication policy can include at least one rule utilized to replicate the data to a remote system. Further, the method includes, based on an acceptance of the replication policy, facilitating, by the system, the replication of the data to the target system according to the replication policy. In an example, the storage context can include a combination of information indicative of one or more parameters used to define the replication policy.