Operational Data Storage Volumes with Jitter Anomaly Detection
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Solution Overview
Problem
Modern computer systems face challenges in efficiently storing and processing operational data from diverse devices, particularly in distributed or virtualized environments, where data significance varies and requires adaptive storage solutions to manage anomalies and ensure durability and redundancy.
Innovation Solution
A data storage system that allocates multiple storage volumes in a sequence for operational data, allowing for interpolation and anomaly detection using a jitter analyzer, and employs redundancy encoding techniques to ensure durability and redundancy across multiple datacenters, enabling efficient storage, retrieval, and anomaly notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operational data is stored in distributed network storage systems, then data availability and accessibility are improved, but storage complexity and management overhead increase
Solution Approach 1:
The patent segments operational data into distinct types (primary data, operational data, metadata) and implements separate storage strategies for each type. Operational data is stored in a time-series format with automatic retention policies, while primary data uses traditional storage methods. This segmentation reduces overall storage complexity by applying specialized handling to each data category rather than managing all data uniformly across the distributed system.
2Reliability
If redundancy encoding is applied to ensure data durability, then data reliability is improved, but storage space requirements increase
Solution Approach 1:
The patent dynamically adjusts redundancy parameters based on data criticality and access patterns. For operational data with high durability requirements, the system applies stronger redundancy encoding (e.g., erasure codes with higher protection factors), while less critical data receives minimal or no redundancy. This parameter adjustment allows the system to optimize the balance between durability and storage space utilization, avoiding uniform application of heavy redundancy across all data types.
3Speed
If data is interpolated from partial volumes to improve retrieval efficiency, then access speed is improved, but data precision may be reduced
Solution Approach 1:
The patent implements a progressive data retrieval strategy where partial volumes are used to provide immediate interpolated results for time-sensitive operations, while full data sets are retrieved asynchronously for applications requiring complete accuracy. The system allows users to specify their precision requirements, enabling partial action (interpolation from subset of volumes) when speed is critical and full action (retrieval from all volumes) when maximum precision is needed, thus resolving the contradiction between speed and accuracy.
Data Source
AI summary
A system stores data, such as sensor data or other operational data, on a plurality of storage volumes in a sequence so as to allow for interpolations or other approximations of the data using a subset of the storage volumes in response to a request for information regarding that data. For example, a plurality of devices connect to the system to provide operational data, which is then stored in a specified sequence on a specified set of volumes. In response to a request for operational information regarding some or all of the devices, the system reads at least one of the volumes, and approximates the values of the data over a specified period of time. In some embodiments, the data may be buffered prior to storage, and a jitter analyzer determines whether the incoming data is anomalous relative to a baseline, which may be determined using related data sets.