Edge Network Data Resiliency Through Layered Protection Policies
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Solution Overview
Problem
Existing network environments face challenges in maintaining data resiliency, particularly in edge networks, where maintaining redundant data copies incurs significant trade-offs in consistency, cost, latency, and throughput, and only valuable data is often protected, leaving lesser-valued data at high risk during Fault-Attack-Failure-Outage (FAFO) events.
Innovation Solution
Implementing a Resilient Control Network (RCN) that manages data resiliency through a hierarchy of data abstraction layers, employing AI/ML models to recover and reconstruct data using tailored resiliency policies, and employing techniques like replication, re-rendering, and inference to optimize data durability during failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundant data copies are maintained in edge networks, then data resiliency is improved, but cost and storage requirements increase significantly
Solution Approach 1:
The patent segments data into different abstraction layers (raw data, processed data, metadata) and applies different resiliency strategies to each layer. This allows selective protection of only the most critical data portions, reducing overall storage requirements while maintaining essential data resiliency.
Solution Approach 2:
The patent employs intelligent copying mechanisms that create redundant copies only for high-value data identified through AI/ML models, rather than uniformly replicating all data. This selective copying approach maintains data resiliency for critical information while minimizing unnecessary storage consumption.
2Reliability
If redundant data copies are maintained across edge networks, then data resiliency is improved, but consistency management becomes more complex
Solution Approach 1:
The patent applies different consistency policies to different data abstraction layers based on their specific requirements. Critical raw data receives stringent consistency policies, while less critical processed data uses more flexible policies. This localized approach to consistency management reduces overall system complexity.
Solution Approach 2:
The system implements feedback mechanisms through AI/ML models that continuously monitor data changes and automatically coordinate updates across redundant copies. This intelligent feedback loop maintains consistency without requiring complex manual coordination protocols.
3Reliability
If comprehensive data protection is implemented, then data resiliency is improved, but latency increases due to additional processing
Solution Approach 1:
The patent performs preliminary classification of data into abstraction layers and assigns resiliency policies in advance, rather than processing all data uniformly when protection is needed. This preliminary action reduces latency during actual protection events by pre-establishing handling procedures.
Solution Approach 2:
The system applies partial protection actions only to the most critical data layers identified through AI/ML analysis, rather than implementing excessive protection across all data. This selective approach maintains essential resiliency while minimizing processing latency.
4Reliability
If AI/ML models are used for data recovery and reconstruction, then data resiliency is improved, but computational requirements and cost increase
Solution Approach 1:
The patent segments the data recovery process into different abstraction layers, applying AI/ML models only to the most critical layers where reconstruction provides maximum benefit. This segmented approach reduces overall computational requirements compared to applying AI/ML uniformly across all data.
Solution Approach 2:
The system applies different computational intensities to different data layers based on their recovery criticality. High-value raw data receives intensive AI/ML-based recovery, while less critical processed data uses simpler reconstruction methods, optimizing the balance between recovery capability and computational requirements.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed for data resiliency in an edge network environment. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to at least one of execute and/or instantiate the instructions to generate spectrum metadata based on spectrum data, determine a resiliency operation based on one or more resiliency requirements, generate a resiliency policy based on at least one of the resiliency operation or the one or more resiliency requirements, generate a resiliency operation map based on at least one of the resiliency policy or first identifiers of respective workloads associated with the network environment, the first identifiers including a second identifier, and, in response to identifying a FAFO event associated with the second identifier, execute the resiliency operation based on mapping the second identifier to the resiliency operation in the resiliency operation map.