Backup Data Distribution Plan Optimization
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Solution Overview
Problem
Current data backup systems lack an efficient method to determine optimal distribution plans for backup data across multiple data centers, leading to suboptimal recovery times and costs, and often require guesswork due to the absence of analytical solutions that consider constraints like recovery point objective (RPO), recovery time objective (RTO), and cost factors.
Innovation Solution
A system that generates a distribution plan using a parameter component and a constraint satisfaction component, which receives a protection level parameter and determines the number of encoded fragments to distribute across data centers, employing erasure coding and linear programming to satisfy constraints such as RPO, RTO, and cost objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backup data is distributed across multiple data centers without an analytical distribution plan, then data recoverability is improved, but cost and recovery time cannot be optimized
Solution Approach 1:
The patent transforms the data distribution problem into a mathematical optimization problem by defining parameters such as protection level (k), number of data centers (n), and encoding schemes. The constraint satisfaction component uses these parameters to generate optimal distribution plans that satisfy multiple constraints simultaneously, converting an complex engineering problem into a solvable mathematical model.
2Reliability
If more encoded fragments are distributed to ensure data recoverability, then reliability improves, but storage cost and network bandwidth increase
Solution Approach 1:
The patent applies erasure coding where only k out of n encoded fragments are needed to recover the original data. This means storing slightly more data than the minimum (excessive action) ensures recoverability even if some fragments are lost, while the mathematical optimization prevents storing unnecessarily excessive amounts. The distribution plan optimizes the ratio of stored fragments to original data size.
3Reliability
If data is replicated to multiple geographic locations for disaster recovery, then reliability improves, but the system complexity and coordination overhead increase
Solution Approach 1:
The patent segments the backup data into multiple encoded fragments and distributes them to different data centers. Each data center stores only a portion of the encoded data, and the system provides disaster recovery by requiring fragments from multiple locations. This segmentation reduces the coordination complexity compared to replicating complete backups, as the system only needs to collect k fragments rather than manage full replicas.
4Productivity
If an analytical solution is implemented to optimize distribution plans, then productivity and cost-efficiency improve, but the computational complexity increases
Solution Approach 1:
The patent replaces manual or heuristic methods of creating distribution plans with an analytical mathematical model. The constraint satisfaction component uses linear programming and optimization algorithms to automatically generate optimal distribution plans, substituting computational mathematics for manual planning processes. This increases initial system complexity but dramatically improves long-term efficiency and consistency.
Data Source
AI summary
A distribution plan that indicates how to encode and distribute backup data across multiple data centers may be generated. The distribution plan may be generated such that one or more characteristics of the distribution plan, such as costs, are optimized while constraints on the plan, such as protection level, recovery point objective (RPO), and recovery time objective (RTO) are satisfied. The protection level may indicate the number of the data centers that are to remain available such that the backup data is recoverable from encoded fragments of the backup data stored in the data centers that remain available.