Dynamic Backup Policy Execution for Multi-Tenant Cluster Storage
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Solution Overview
Problem
Conventional systems lack the capability to dynamically calculate and execute data backup policies for multi-tenant cluster storage, leading to inefficient memory and processing usage, as they apply the same backup policies to all applications.
Innovation Solution
A system that dynamically calculates individual backup policy schedules for each folder of applications in a multi-tenant cluster storage by accessing audit logs, footfall databases, and using machine learning to determine criticality, thereby executing tailored backup policies and ad hoc backups based on real-time data and user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional systems apply the same backup policies to all applications, then backup execution is simplified, but memory and processing resources are wasted on unnecessary backups
Solution Approach 1:
The system segments backup policies by application folder criticality levels. Audit logs are analyzed to divide applications into different criticality categories (high, medium, low), and separate backup schedules are created for each category. This segmentation allows the system to apply appropriate backup frequency to each segment rather than using a uniform policy, reducing unnecessary backups for low-criticality applications while maintaining adequate protection for high-criticality ones.
Solution Approach 2:
The system implements local quality by assigning different backup policy characteristics to different application folders based on their specific criticality levels. Each application folder receives a customized backup schedule tailored to its actual data importance and change frequency, rather than applying a single global policy. This localized approach optimizes resource allocation by matching backup intensity to actual need at each specific location in the storage hierarchy.
2Loss of energy
If the system dynamically calculates individual backup policies for each application folder, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing criticality levels for each application folder based on historical audit log analysis. These pre-computed criticality metrics are cached and reused for backup schedule determination, avoiding the need to perform complex real-time analysis whenever a backup decision is needed. This preliminary computation reduces the complexity of real-time backup policy execution while maintaining dynamic adaptability.
Solution Approach 2:
The system implements self-service by automatically analyzing audit logs and footfall data to determine application folder criticality without requiring manual intervention. The system autonomously generates backup policies based on observed data patterns, eliminating the need for administrators to manually configure complex backup schedules for each application. This self-service capability reduces operational complexity while enabling fine-grained, data-driven backup optimization.
3Loss of energy
If backup policies are made dynamic and customized per application, then unnecessary backups are reduced, but the difficulty of detecting and measuring criticality increases
Solution Approach 1:
The system introduces an intermediary mechanism by using audit logs and footfall data as intermediate metrics to infer application folder criticality. Rather than directly measuring complex criticality attributes, the system monitors intermediate indicators such as data access frequency, change patterns, and user activity metrics. These intermediate measurements serve as proxies for criticality, making the assessment process more tractable while still enabling differentiated backup policies.
Solution Approach 2:
The system implements feedback by continuously monitoring audit logs and adjusting backup policies based on observed data patterns. The system measures actual data access and modification behavior, compares it against current backup schedules, and dynamically adjusts policies to match observed criticality levels. This closed-loop feedback mechanism simplifies criticality measurement by using actual operational data rather than requiring complex predictive models, while still achieving optimized backup resource allocation.
Data Source
AI summary
Embodiments of the present invention provide a system for calculating and executing data backup policies for a multi-tenant cluster storage. The system is configured for accessing one or more audit logs associated with one or more applications, where the one or more applications comprise one or more folders, accessing a footfall database to identify footfall data associated with the one or more applications, determining criticality of the one or more folders associated with the one or more applications based on the footfall data, determining a dynamic backup policy schedule for the one or more applications, storing the dynamic backup policy schedule in a backup policy database, based on the dynamic backup policy schedule, determining that at least one application of the one or more applications needs backup at a current time, and executing a backup policy and take a backup of the at least one application.