Feature Processing Units for Cloud-Edge Data Consistency
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Solution Overview
Problem
There is a delay in updating data analytics software at edge devices due to software installation, testing, and compliance policies, leading to inconsistent results and data analysis errors between the service provider and edge devices.
Innovation Solution
The implementation of feature processing units (FPUs) with a data processing abstraction API allows for the continuous deployment and orchestration of FPUs in a managed cloud and provider network, enabling consistent data processing across both environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software update processes are used at edge devices, then software installation and compliance policies are followed, but significant delays occur before updated software is installed
Solution Approach 1:
The software is segmented into modular feature processing units (FPUs) that can be independently deployed and updated. Each FPU represents a discrete functional component that can be developed, tested, and deployed separately, allowing incremental updates without requiring complete software reinstallation at edge devices.
Solution Approach 2:
Feature processing units are developed, tested, and validated in advance in the cloud environment before being deployed to edge devices. The cloud service provider prepares and qualifies FPUs beforehand, so that when deployment occurs at edge devices, the process is rapid and compliant with minimal on-site testing requirements.
2Reliability
If traditional software deployment methods are used, then compliance with installation policies is maintained, but different versions of software exist between cloud and edge devices causing inconsistent results
Solution Approach 1:
The system implements dynamic version management where the cloud service provider can push updated FPUs to edge devices in real-time. Edge devices automatically receive and activate new versions, ensuring both cloud and edge environments run identical software versions. This dynamic update mechanism eliminates version drift while maintaining deployment compliance.
Solution Approach 2:
The system incorporates feedback mechanisms where the cloud service provider monitors FPU performance and deployment status across edge devices. This feedback loop enables the provider to verify consistent execution of FPUs across the distributed system and to push updates uniformly, ensuring data processing consistency between cloud and edge environments.
3Reliability
If comprehensive testing and qualification cycles are performed before software updates, then compliance is ensured, but lengthy qualification cycles delay feature deployment
Solution Approach 1:
Extensive testing and qualification of feature processing units are performed in advance in the cloud environment before deployment to edge devices. The cloud service provider maintains a qualified repository of FPUs that have already undergone comprehensive validation, so edge devices can deploy pre-validated units without repeating lengthy qualification cycles.
Solution Approach 2:
The system performs comprehensive testing in the cloud environment (excessive action) before deployment, allowing minimal or targeted validation at edge devices (partial action). This approach ensures full compliance through thorough upfront testing while enabling rapid deployment at edge locations without repeating the entire qualification process.
Data Source
AI summary
A feature deployment service of a provider network may deploy feature processing units (FPUs) to implement data processing features at both a provider network and edge devices. The use of FPUs may allow a client to use new features at the edge, without delays due to compliance/testing or software upgrades. An FPU includes a model and compute logic that are used to implement a data processing feature. A feature processing service deploys the FPU to an FPU engine at the provider network and also deploys the FPU to edge devices of the client's network that each include an edge FPU engine. The FPU engine at the provider network and the edge FPU engine at each edge device conform to a common specification/API, allowing deployment and use of the same FPU/data processing features at both the cloud and the edge.