Smart Polling System for Network Entity Detection
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Solution Overview
Problem
Legacy polling systems in network management create excessive network traffic and congestion by polling all devices at a standard frequency, leading to delays in critical communications and strain on network resources, especially during peak times, as the volume of polling packets increases linearly with the number of devices and can overwhelm routers.
Innovation Solution
A smart polling system that assesses the relative importance of each network entity based on device information and traffic data, assigning higher polling frequencies to critical entities and reducing frequencies for less important ones, dynamically adjusting polling targets and frequencies to prioritize key network entities and reduce unnecessary polling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all network devices are polled at a standard frequency, then complete network monitoring coverage is achieved, but network traffic congestion increases and critical communications are delayed
Solution Approach 1:
The patent applies local quality by assigning different polling frequencies to different network devices based on their criticality. Critical devices receive higher polling frequencies for密切 monitoring, while non-critical devices are polled less frequently. This differentiated approach ensures reliable monitoring of important devices without generating excessive network traffic from polling all devices uniformly.
Solution Approach 2:
The system dynamically changes the polling frequency parameter for each device based on calculated criticality scores. The criticality score is derived from device information and traffic information, and this score directly determines the polling frequency. This parameter adaptation allows the system to maintain monitoring reliability for critical devices while reducing network traffic by lowering polling frequencies for non-critical devices.
2Speed
If polling frequency is increased for all devices, then detection speed of critical entities improves, but network resource strain increases
Solution Approach 1:
The patent implements local quality by applying high polling frequencies only to critical network devices that require fast detection, while using lower polling frequencies for non-critical devices. This localized high-frequency polling achieves fast detection speed for important entities without causing network resource exhaustion from uniformly high-frequency polling of all devices.
Solution Approach 2:
The system applies excessive action (high polling frequency) only partially to the subset of critical devices that truly require it, rather than applying excessive polling to all devices. This partial excessive action achieves rapid detection for critical entities while avoiding unnecessary network resource consumption from over-polling non-critical devices.
3Loss of energy
If polling frequency is decreased to reduce network traffic, then network resource usage is optimized, but detection precision of critical entities deteriorates
Solution Approach 1:
The patent applies local quality by maintaining high polling frequencies for critical devices to preserve detection precision, while reducing polling frequencies for non-critical devices to optimize network resource usage. This differentiated approach ensures that detection precision is maintained where needed without sacrificing overall network efficiency.
Solution Approach 2:
The system applies sufficient or excessive polling action only to critical devices that require high detection precision, while using reduced polling for non-critical devices. This partial application of high-frequency polling maintains measurement precision for important entities while optimizing network resource consumption across the entire network.
4Device complexity
If standard polling is used for all devices, then system complexity is low, but productivity of network management decreases
Solution Approach 1:
The patent changes the polling frequency parameter dynamically based on device criticality scores, which are calculated from device and traffic information. This parameter adaptation improves network management productivity by focusing monitoring resources on critical devices, enabling faster detection and response to issues while the system maintains manageable complexity through automated score-based decision making.
Data Source
AI summary
A method includes receiving, for each of a plurality of entities in a network, device information associated with the entity, and network traffic information regarding data packets associated with the entity. The method also includes determining, using a processor, a priority score for each of the plurality of entities in the network based on the device information and the traffic information. The method additionally includes determining a polling frequency for each of the plurality of entities based on each priority score, wherein the polling frequency for a first entity having a first priority score is greater than the polling frequency for a second entity having a second priority score that is lower than the first priority score. The method further includes polling each of the plurality of entities at its polling frequency.