Automated Recovery Schedule Optimization
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Solution Overview
Problem
Current disaster recovery methods are inefficient in determining optimal recovery schedules, often resulting in delayed and incomplete system restoration due to reliance on manual rules of thumb and lack of detailed practical guidance, leading to increased financial penalties and vulnerability during data recovery operations.
Innovation Solution
A recovery scheduling system that automates the determination of recovery schedules by formalizing a recovery graph as an optimization problem, utilizing user-provided criteria and penalty rates, and applying solution techniques such as mixed integer programming, genetic algorithms, or hybrid approaches to minimize financial penalties and ensure efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual rules of thumb are used to determine recovery schedules, then the process is simple to implement, but the recovery schedule is inefficient and results in delayed system restoration
Solution Approach 1:
The patent replaces manual mechanical scheduling processes with an automated computer system that uses optimization algorithms (mixed integer programming, genetic algorithms) to determine recovery schedules. This substitution eliminates human error and manual intervention while significantly improving recovery efficiency and minimizing downtime.
Solution Approach 2:
The system enables self-service by automatically determining optimal recovery schedules without requiring manual input or intervention. The optimization system independently analyzes recovery options, constraints, and objectives to generate schedules that minimize penalties and maximize recovery efficiency, allowing the system to serve itself rather than requiring continuous human management.
2Reliability
If traditional recovery plans are updated to reflect system changes, then the plans remain accurate and relevant, but the updating process is time-consuming and increases penalty costs
Solution Approach 1:
The patent implements preliminary action by maintaining an up-to-date inventory of recovery options and system constraints in advance. The system continuously monitors and updates its knowledge base of recovery methodologies, storage locations, and resource requirements, so that when a disaster occurs, the optimization system can immediately generate an accurate recovery schedule without requiring time-consuming updates during the critical recovery period.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor system changes, disaster recovery progress, and penalty implications. This feedback loop allows the optimization system to learn from actual recovery scenarios and adjust its algorithms accordingly, improving future schedule determinations while minimizing the need for manual plan revisions.
3Productivity
If recovery schedules are determined without considering multiple constraints and objectives, then the schedule is easy to compute, but the financial penalties and data loss increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting optimization parameters such as penalty rates, recovery priorities, and resource allocation based on the specific disaster scenario and system state. The mixed integer programming and genetic algorithms modify key parameters like recovery time windows, data loss tolerance levels, and resource constraints to generate schedules that minimize financial penalties while maintaining computational efficiency.
Solution Approach 2:
The system implements dynamics by creating a flexible recovery scheduling framework that can adapt to changing conditions during the recovery process. The optimization algorithms dynamically adjust recovery sequences, resource allocations, and timeline estimates based on real-time information about system status, available resources, and emerging constraints, ensuring optimal schedules that minimize penalties across varying scenarios.
Data Source
AI summary
Provided is a method for determining a recovery schedule. The method includes accepting as input a recovery graph. The recovery graph presents one or more strategies for data recovery. In addition, at least one objective is provided and accepted. The recovery graph is formalized as an optimization problem for the provided objective. When formalized as an optimization problem, at least one solution technique is applied to determine at least one recovery schedule.