Dynamic Data Collection Optimization in Managed Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional managed networks with digital experience (DEX) platforms face challenges in optimizing data collection, as the static nature of data collection elements does not adapt to dynamic changes in computing devices and user circumstances, leading to increased computing resources overhead.
Innovation Solution
A method for data collection optimization in managed networks with a DEX platform, which involves collecting data from endpoints based on dynamic criteria. This includes identifying device context data indicating specific events, modifying collection frequency and verbosity accordingly, and reverting to baseline criteria when events no longer exist.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection frequency and verbosity are increased to improve accuracy of user experience assessment, then measurement precision is improved, but computing resources overhead increases
Solution Approach 1:
The patent implements dynamic data collection by continuously monitoring device context data (battery level, network security, device state) and adjusting collection frequency and verbosity in real-time. When devices are in stable states, collection occurs at baseline frequency; when events are detected (low battery, unsecured network), the system dynamically modifies collection parameters to balance accuracy needs with resource conservation.
Solution Approach 2:
The system changes collection parameters (frequency and verbosity) based on detected device context data. Collection frequency transitions between baseline and increased rates, and verbosity adjusts between standard and enhanced detail levels, depending on the device state and detected events, thereby optimizing the balance between measurement precision and resource consumption.
2Ease of operation
If static data collection elements are used to simplify system operation, then ease of operation is improved, but adaptability to dynamic device circumstances deteriorates
Solution Approach 1:
The system implements self-service through automated event detection and response mechanisms. Device agents continuously monitor local context data and autonomously determine when to increase or decrease collection intensity based on predefined event criteria, eliminating the need for manual configuration adjustments while maintaining high adaptability to changing device circumstances.
Solution Approach 2:
The patent establishes a feedback loop where collected device context data is analyzed to automatically adjust future collection behavior. The system receives feedback from device state monitoring and uses this information to dynamically modify collection parameters, enabling adaptability while maintaining operational simplicity through automated closed-loop control.
Data Source
AI summary
An embodiment includes a method of data collection optimization in a managed network having a digital experience platform that includes collecting data from managed endpoints using first collection criteria. The first collection criteria include a first frequency and a first verbosity. The method includes identifying, in the collected data, device context data that indicates a defined event exists relative to an endpoint. The collected data or the device context data are used to compute a digital experience index. Responsive to the identified device context data, the method includes modifying the first frequency or the first verbosity to implement a second collection criteria relative to a subset of managed endpoints; collecting additional data from the subset of managed endpoints using the second collection criteria; receiving additional context data that indicates the defined event no longer exists; and in response, collecting data from the managed endpoints using the first collection criteria.