Cloud Pipeline Data Prepositioning for Distributed User Access
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Solution Overview
Problem
Cloud-based systems face latency issues when distributing data to geographically distributed users in production pipelines, leading to inefficiencies and frustration due to slow file migration.
Innovation Solution
A system representing production pipelines as flow graphs of tasks and data, proactively gathering and delivering data to users based on their tasks, using reduced-fidelity data when necessary, and employing AI to predict data needs and network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is migrated between geographically distributed users in a cloud-based production pipeline, then data can be accessed by multiple users, but latency increases and efficiency decreases
Solution Approach 1:
The system proactively transfers data from storage to edge locations before users actually need it, using AI predictions to anticipate data requirements. This preliminary action eliminates waiting time when users log in, as resources are already positioned and ready for immediate access.
Solution Approach 2:
The patent introduces edge locations as intermediary nodes between central cloud storage and end users. These edge servers act as mediators that cache and pre-position data locally, reducing the distance and time for data transmission while maintaining centralized control over data distribution.
2Manufacturing precision
If full-fidelity data is transferred to users, then data quality is maintained, but transfer time increases due to network latency
Solution Approach 1:
The system applies different data fidelity levels to different users based on their specific needs and context. AI predictions determine which users require full-fidelity data and which can work with reduced-fidelity versions, allowing localized optimization of data quality versus transfer speed for each user scenario.
Solution Approach 2:
The patent dynamically changes the fidelity parameter of data transfers based on predicted user needs. When AI predicts a user will need data soon, the system adjusts the transfer parameters to prioritize speed using reduced-fidelity versions, while maintaining the option to provide full-fidelity data when time permits or quality is critical.
3Speed
If data is pre-positioned at edge locations, then user access speed improves, but network bandwidth consumption increases
Solution Approach 1:
The system uses AI predictions and user behavior patterns to make intelligent decisions about which data to pre-position at edge locations. This feedback mechanism ensures that bandwidth is consumed only for data that is actually likely to be accessed, avoiding wasteful pre-positioning of unused data while still improving access speed for predicted needs.
Data Source
AI summary
The system efficiently distributes data in a cloud pipeline to geographically distributed users. To enable efficient distribution of resources to multiple users, the production pipeline can be represented as two flow graphs: a flow graph of tasks as well as a flow graph of data. The flow graph of tasks can indicate a sequence of tasks, departments, and users in a production pipeline. The flow graph of data can indicate location of data needed for each task in the flow graph of tasks, and location of the users assigned to the particular task. Based on the next task that needs to be executed in the flow graph of tasks, the system can proactively gather the data from the various locations and deliver the data to the users assigned to executing the next task.