Entropy-Based Device Utilization Imbalance Detection
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Solution Overview
Problem
Existing computer systems face challenges in automatically detecting and addressing imbalanced utilization among groups of devices with similar capabilities, leading to sub-optimal performance and resource wastage, as manual inspection is laborious and time-consuming.
Innovation Solution
A method that monitors device statistics over time, calculates an entropy value to quantify utilization imbalance, and generates alerts when imbalances exceed a configured threshold, allowing for automatic detection and alerting of imbalanced device groups, thereby enabling timely rebalancing and improving system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to detect device utilization imbalances, then detection accuracy can be achieved, but the process becomes laborious and time-consuming
Solution Approach 1:
The system automatically monitors device statistics and detects utilization imbalances without human intervention. The monitoring component continuously collects data from devices, and the entropy calculation component automatically processes this data to identify imbalances, eliminating the need for manual inspection while maintaining detection accuracy.
Solution Approach 2:
The patent replaces manual mechanical inspection with an automated computational system. Instead of engineers manually examining device statistics, the system uses software components to automatically collect, process, and analyze device data, substituting human effort with automated information processing.
2Reliability
If extensive manual checks are performed to identify imbalanced devices, then comprehensive detection can be achieved, but resource wastage increases due to the time and effort required
Solution Approach 1:
The system performs self-monitoring and self-diagnosis, automatically detecting device imbalances without requiring external human resources. The monitoring and analysis components continuously operate autonomously to identify issues, eliminating the need for extensive manual checks and reducing resource consumption.
Solution Approach 2:
The system implements continuous feedback loops where device statistics are constantly monitored, analyzed for imbalances, and used to trigger alerts. This automated feedback mechanism ensures comprehensive detection while minimizing resource waste by only activating full analysis when necessary.
3Productivity
If automated monitoring is implemented to detect device imbalances, then detection speed improves, but system complexity increases
Solution Approach 1:
The monitoring system is divided into distinct functional components: a monitoring component that collects device statistics, an entropy calculation component that processes the data, and an alerting component that notifies users. This segmentation allows each component to perform its specific function efficiently, improving detection speed while managing complexity through modular design.
Solution Approach 2:
The patent introduces an entropy value as an intermediary metric that simplifies the detection process. Instead of directly analyzing complex device utilization patterns, the system calculates entropy values that represent imbalance levels, providing a simplified intermediate representation that speeds up detection while reducing the complexity of direct analysis.
Data Source
AI summary
Embodiments monitor statistics from groups of devices and generate an alarm upon detecting a utilization imbalance that is beyond a threshold. Particular balance statistics are periodically sampled, over a timeframe, for a group of devices configured to have balanced utilization. The devices are ranked at every data collection timestamp based on the gathered device statistics. The numbers of times each device appears within each rank over the timeframe are tallied. The device/rank summations are collectively used as a probability distribution representing the probability of each device being ranked at each of the rankings in the future. Based on this probability distribution, an entropy value that represents a summary of the imbalance of the group of devices over the timeframe is derived. An imbalance alert is generated when one or more entropy values for a group of devices shows an imbalanced utilization of the devices going beyond an identified imbalance threshold.