Distributed Rule Evaluation for UEM Bandwidth Reduction
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Solution Overview
Problem
Existing unified endpoint management (UEM) solutions face challenges in efficiently verifying device compliance across hundreds to thousands of client devices, leading to high bandwidth and processing time consumption, and scalability issues as the management service must periodically check-in and perform tasks on large numbers of devices.
Innovation Solution
Delegating processing of device data to remotely-hosted web services, including an event-driven web service, a rule evaluation service, and a rule action service, which utilize on-demand cloud computing platforms to distribute and scale the processing workload, reducing the burden on the management service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the management service periodically checks in with hundreds to thousands of devices and performs tasks on received data, then device compliance can be verified, but bandwidth consumption and processing time increase significantly
Solution Approach 1:
The patent extracts the data processing functionality from the central management service and places it at the edge devices themselves. Each device performs rule evaluations locally on its own data, eliminating the need to transmit large amounts of raw data to the management service for processing, thus reducing bandwidth consumption while maintaining compliance verification capability
Solution Approach 2:
The patent introduces event-driven web services as intermediaries between devices and the management service. These web services receive data from devices, perform rule evaluations, and only communicate results or significant events back to the management service, reducing the bandwidth burden compared to continuous data transmission
2Reliability
If the management service periodically checks in with hundreds to thousands of devices and performs tasks on received data, then device compliance can be verified, but processing time consumption increases
Solution Approach 1:
The patent extracts the computationally intensive rule evaluation processing from the central management service and executes it locally at each device. This eliminates the time required to transmit data to and from the management service for processing, and allows parallel evaluation at multiple devices simultaneously, significantly reducing overall processing time
Solution Approach 2:
The patent implements preliminary action by having devices continuously monitor and evaluate their own data against compliance rules in real-time, rather than waiting for periodic check-ins from the management service. This ensures compliance verification occurs immediately when data changes, reducing the time delay inherent in periodic polling approaches
3Adaptability or versatility
If the management service oversees hundreds to thousands of devices, then comprehensive device management is achieved, but scalability becomes difficult
Solution Approach 1:
The patent segments the monolithic management architecture into distributed components: each device runs its own rule evaluation engine locally, and event-driven web services handle specific device types or functions. This segmentation allows the system to scale horizontally by adding more independent evaluation nodes without increasing the complexity burden on a central service
Solution Approach 2:
The patent implements universality through a standardized rule evaluation framework that can handle multiple device types, protocols, and compliance requirements through a common architecture. The event-driven web services can be configured to serve different device categories, allowing the system to expand to new device types without fundamental architectural changes
Data Source
AI summary
Various examples for performing rule processing for device management data evaluation are described. According to some examples, one or more computing devices can receive new device data from a client device indicative of a current configuration of the client device and identify a change of a device configuration value from a past device configuration value by comparing the new device data to historic device data. The computing devices can send the new device data to a rule evaluation service to identify one or more rules associated with the device configuration value a rule action service to perform an action specified in the rules. The rule action service can determine the action to perform based on the device configuration value.