Dynamic Network Deployment Thresholds for Device Segmentation
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Solution Overview
Problem
Determining a single deployment threshold for all network devices in a network can be inefficient, as different devices experience varying deployment times based on their characteristics and associated servers, leading to potential delays or inefficiencies.
Innovation Solution
Implementing a system where dynamic deployment thresholds are determined for each type of network device and server, allowing for adjustments based on deployment data and network changes, such as modifying thresholds if a significant number of devices take longer than the initial threshold to deploy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single deployment threshold is used for all network devices, then the deployment process is simple to manage, but deployment efficiency decreases due to varying device characteristics and server associations
Solution Approach 1:
The patent segments the network devices into different types (e.g., lightweight APs, full-featured APs, wireless controllers) and assigns specific deployment thresholds to each type. This segmentation allows the system to account for varying device characteristics and server associations, thereby improving deployment efficiency without significantly increasing overall system complexity.
Solution Approach 2:
The patent implements dynamic deployment thresholds that can be adjusted based on deployment data and network changes. The system monitors deployment performance and automatically modifies thresholds for different device types, transforming a static single-threshold approach into a dynamic multi-threshold system that adapts to changing conditions.
2Manufacturing precision
If deployment thresholds are customized for each device type and server, then deployment accuracy improves, but system complexity increases
Solution Approach 1:
The system segments devices into distinct types with specific characteristics and server associations. Each segment receives customized deployment thresholds, improving accuracy while managing complexity through organized categorization rather than individual customization for every device.
Solution Approach 2:
The patent changes the deployment threshold parameter based on device type and server characteristics. By systematically varying this key parameter across different device categories, the system achieves higher deployment accuracy without requiring complex individualized configurations for each device.
3Adaptability or versatility
If dynamic threshold adjustments are implemented based on deployment data, then deployment adaptability improves, but processing requirements increase
Solution Approach 1:
The system implements dynamic threshold adjustments that adapt to deployment data and network changes. By making the deployment thresholds flexible and responsive to actual performance data, the system achieves high adaptability while processing requirements remain manageable through automated monitoring and adjustment mechanisms.
Solution Approach 2:
The patent incorporates feedback loops where deployment data is collected, analyzed, and used to adjust deployment thresholds for future deployments. This feedback mechanism enables continuous improvement and adaptation without requiring excessive processing power, as the system learns from past deployment outcomes.
Data Source
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AI summary
Example implementations relate to network deployment of devices. For example, a non-transitory computer readable medium storing instructions executable by a processing resource can determine a plurality of deployment thresholds of a plurality of devices, wherein the plurality of deployment thresholds are associated with a type of the plurality of devices. The instructions can cause the processing resource to monitor deployment data associated with the plurality of devices to identify a device with a deployment outlier. The device with the deployment outlier is a device with deployment data that is outside a deployment threshold of the device. The instructions can cause the processing resource to adjust the deployment threshold of the device based on the monitoring.