Adjustable Edge Execution Models for Secure Usage Data Processing
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Solution Overview
Problem
Conventional techniques for collecting and processing usage data from remotely managed infrastructure face challenges such as inefficiency, high computational resource requirements, and data security and compliance issues, particularly when dealing with diverse customer subscriptions and geographical variations.
Innovation Solution
Implementing a set of function blocks on customer locations to process usage data, using a machine learning model to automatically select and orchestrate these blocks based on subscription details, reducing the need for additional hardware resources and enhancing data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If usage data is collected at customer location and sent to central location for processing, then data processing can be performed, but processing speed is slow and efficiency is reduced
Solution Approach 1:
Instead of collecting data at the customer location and sending it to a central location for processing (conventional approach), the patent inverts the processing location to the edge device at the customer premises. The edge device executes function blocks locally to process usage data, eliminating the time-consuming data transmission and central processing bottleneck while maintaining accurate billing and reporting capabilities.
2Reliability
If conventional data collection techniques are used, then usage data can be processed, but data security and compliance issues arise
Solution Approach 1:
The patent segments the data processing function into distributed edge devices at customer locations rather than centralized processing. Each edge device processes usage data locally using function blocks, which eliminates the security risks associated with transmitting sensitive usage data across networks. This segmentation maintains data security and compliance while enabling accurate billing and reporting.
3Adaptability or versatility
If diverse customer subscriptions and geographical variations are handled, then service coverage is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal function block architecture where a single edge device can execute different function blocks to handle diverse customer subscriptions and geographical variations. The function blocks are configurable and can be dynamically selected based on subscription type and location requirements, providing service versatility without increasing hardware complexity. This modular approach allows the same edge device infrastructure to serve multiple service types and regions.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for processing usage data for remote infrastructure using adjustable execution models are provided herein. An example computer-implemented method includes: obtaining a data structure related to a subscription request for hardware infrastructure provided by a service provider, wherein the data structure is obtained by a processing engine deployed on the hardware infrastructure at a remote location; analyzing the data structure to build an execution model comprising function blocks for processing usage data associated with the hardware infrastructure and one or more instructions to execute the function blocks; processing the usage data for a given time period in accordance with the execution model; providing execution data associated with the plurality of function blocks; and automatically adjusting the execution model for an additional time period using information obtained from the service provider, wherein the obtained information is based at least in part on the execution data.


