Dynamic Backup Policy Adaptation for Restore Performance
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Solution Overview
Problem
Conventional data backup systems employ static backup policies that fail to adapt to changes in client usage patterns and hardware characteristics, leading to inadequate restore performance and unmet service-level agreements (SLAs) due to inefficient resource allocation and sequential restoration processes.
Innovation Solution
Implement dynamic modification of data backup software policies based on user-defined conditions and underlying hardware changes, ensuring balanced resource utilization and cohesive end-to-end results that meet client expectations by adjusting backup frequency, storage location, and resource allocation in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static backup policies are used, then backup workflows are simple to manage, but restore performance is inadequate and service-level agreements cannot be met
Solution Approach 1:
The patent implements dynamic backup policies that automatically adjust backup workflows, resource allocation, and restore operations based on real-time system conditions, data criticality levels, and performance metrics. This allows the system to optimize restore performance during critical events while adapting policy complexity only when necessary, rather than maintaining static high-complexity policies continuously.
Solution Approach 2:
The system modifies backup policy parameters dynamically based on changing conditions, including adjustment of backup frequency, resource allocation levels, and restore priority settings. These parameter changes enable the system to meet service-level agreements during critical events while maintaining simpler operation during normal conditions.
2Loss of time
If sequential restoration processes are used, then resource allocation is simplified, but restore time is excessive and client expectations are not met
Solution Approach 1:
The patent divides the restoration process into multiple parallel streams that can simultaneously restore different data sets or components. This segmentation enables concurrent restoration operations, significantly reducing total restore time while maintaining manageable complexity through structured organization of restoration tasks.
Solution Approach 2:
The system dynamically adjusts the number and configuration of parallel restoration streams based on system conditions, data criticality, and available resources. During critical restore events, the system activates multiple parallel streams to minimize restore time, while under normal conditions it uses fewer streams to reduce complexity.
3Reliability
If backup policies do not adapt to hardware changes, then policy management is straightforward, but restore effectiveness deteriorates due to inefficient resource allocation
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor system conditions, hardware characteristics, and restore performance metrics. This feedback enables automatic adjustment of backup policies to adapt to hardware changes, ensuring restore effectiveness is maintained without requiring manual policy updates or complex manual adaptation processes.
Solution Approach 2:
The system automatically detects hardware changes and self-adjusts backup policies without requiring manual intervention. This self-service capability maintains policy adaptability to hardware changes while keeping policy management straightforward, as the system handles adaptations autonomously based on monitored conditions.
4Speed
If multiple parallel restoration streams are used, then restore speed is improved, but system resource contention increases and stability decreases
Solution Approach 1:
The patent dynamically controls the number and resource allocation of parallel restoration streams based on real-time system conditions, available resources, and data criticality. This dynamic adjustment enables the system to achieve high restore speeds when conditions permit while preventing resource contention and maintaining stability when resources are constrained.
Solution Approach 2:
The system modifies parameters of restoration streams including throughput rates, priority levels, and resource allocation based on system conditions. These parameter changes enable parallel streams to operate at high speeds when resources are abundant while automatically throttling to maintain system stability when resources are limited.
Data Source
AI summary
Disclosed is a method, apparatus, and system for dynamically changing a backup policy, the operations comprising: automatically detecting a change in a backup source system; automatically activating a new backup policy, wherein the new backup policy is determined based on the change in the backup source system and an old backup policy; and performing a backup session based on the new backup policy.