Virtual Memory Vaults for Adaptive Storage Network Provisioning
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Solution Overview
Problem
Existing data storage systems face challenges in dynamically adjusting resource provisioning to match changing needs, leading to inefficiencies in data access performance over time.
Innovation Solution
A dispersed storage network (DSN) with a managing unit that performs adaptive resource provisioning based on estimated future loading, using error encoding and decoding techniques like Cauchy Reed-Solomon, and a neural network model for predictive performance modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed resource provisioning is used in storage systems, then initial needs are met, but performance degrades as needs change over time
Solution Approach 1:
The patent implements dynamic resource provisioning by continuously monitoring system performance metrics and adjusting resource allocation in real-time. The system transitions from static fixed provisioning to dynamic adaptive provisioning that responds to changing workload conditions, ensuring both reliability and sustained productivity.
Solution Approach 2:
The system employs feedback mechanisms by monitoring performance metrics and using this information to adjust resource provisioning decisions. The feedback loop enables the system to detect performance degradation and automatically provision additional resources or optimize resource utilization to maintain adequate service levels.
2Productivity
If additional resources are provisioned to meet future needs, then performance is maintained, but resource waste occurs when needs are overestimated
Solution Approach 1:
The system performs preliminary actions by predicting future resource needs based on historical data and trends before actual performance degradation occurs. This allows proactive resource provisioning that prevents performance issues while avoiding excessive resource allocation by base provisioning on accurate predictions rather than worst-case scenarios.
Solution Approach 2:
The system dynamically changes resource provisioning parameters based on actual system conditions and performance metrics. Rather than using fixed provisioning levels, the system adjusts parameters such as resource allocation ratios, caching strategies, and load distribution to optimize both performance and resource utilization efficiency under varying conditions.
3Productivity
If complex adaptive provisioning is implemented, then performance is optimized, but system complexity increases
Solution Approach 1:
The system implements self-service capabilities by enabling automated resource provisioning decisions based on monitored performance metrics and predictive models. The system autonomously detects performance issues, determines appropriate provisioning actions, and executes adjustments without requiring complex manual intervention or overly complicated control mechanisms.
Solution Approach 2:
The complex adaptive provisioning system is segmented into modular functional components such as monitoring modules, prediction modules, decision modules, and execution modules. This segmentation allows each component to perform a specific function independently, making the overall complex system more manageable, maintainable, and easier to implement while still achieving optimized performance.
Data Source
AI summary
A method for execution by a storage network begins by creating a plurality of vaults in memory addressable by the storage network, where a vault is a virtual memory block associated with a portion of storage network memory, and then determining dispersed storage error encoding parameters for each vault. The method then continues by creating a plurality of vault regions within each vault, wherein a vault region is configured to store a data type of a plurality of data types, where at least one data type of the plurality of data types is associated with a unique access restriction.