Endpoint Resource Allocation via User Behavior Clustering
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Solution Overview
Problem
IT administrators face challenges in providing user-oriented optimizations for various applications due to difficulties in identifying and maintaining process lists and tuning optimization parameters to comply with individual user behaviors, as existing endpoint resource optimization approaches are not effectively automated.
Innovation Solution
The implementation of automated app optimization using unsupervised machine learning to divide users into groups based on cluster modeling of usage activity data, determining application priorities and computing resource allocations, and refining these allocations over time based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual identification and maintenance of process lists is used for endpoint resource optimization, then customization to user behaviors is possible, but IT administrator workload and system complexity increase
Solution Approach 1:
The system automatically performs user behavior analysis, application identification, and resource optimization without requiring manual IT administrator intervention. The endpoint device self-monitors usage patterns, self-identifies applications, and self-optimizes resource allocations based on learned behaviors, eliminating the need for manual process list maintenance while maintaining high adaptability to user behaviors
Solution Approach 2:
The patent replaces manual mechanical processes (IT administrators manually creating and maintaining process lists) with automated machine learning systems. The system uses unsupervised learning algorithms to automatically analyze usage patterns, identify applications, and determine resource optimizations, substituting human effort with intelligent automation that scales without increasing administrative workload
2Ease of operation
If automated resource optimization is implemented, then IT management is simplified, but precision in meeting individual user behavior requirements may be reduced
Solution Approach 1:
The system continuously monitors actual user behaviors and application usage patterns, compares them against learned models, and automatically adjusts resource allocations based on this feedback loop. This closed-loop approach ensures that automated optimization maintains high precision in matching individual user behaviors while keeping IT management simple, as the system self-corrects based on observed performance
Solution Approach 2:
The system performs preliminary analysis of usage patterns during off-peak periods to build accurate user behavior models before optimization is needed. By pre-learning and pre-configuring optimization parameters based on historical data, the system ensures high precision in meeting user requirements when actual optimization is applied, while maintaining ease of operation through automated model building
Data Source
AI summary
A computing device may include a memory and a processor coupled to the memory and configured to collect usage activity data across a plurality of different applications for a plurality of users, and determine different groups of users based upon cluster modeling of the usage activity data. The processor may further determine respective application priorities for the applications for each group of users based upon the usage activity data for the group of users, determine computing resource allocations for the applications of each group of users based upon the application priorities for the group of users, and run applications for the users with the computing resource allocations for the respective group of users applied thereto.