Wireless Client Redistribution for AP Load Balancing
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Solution Overview
Problem
Wireless networks inefficiently allocate resources due to suboptimal distribution of clients, leading to issues such as sticky clients, load balancing problems, and cellular offload inefficiencies.
Innovation Solution
Implement systems and methods for redistributing wireless clients based on connection metrics, using probabilistic calculations and threshold comparisons to optimize association with access points, including disconnect or redirect commands, and provide additional information for client decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If clients select AP based on initial scan, then client association is simple, but network resource allocation is inefficient
Solution Approach 1:
The system performs preliminary actions by having clients conduct initial scans and maintain lists of candidate APs before actual association decisions are made. The network also pre-calculates connection metrics and prepares redistribution decisions in advance, so when association is needed, the optimal AP is already identified, resolving the contradiction between simple association and efficient resource allocation.
Solution Approach 2:
The system implements feedback mechanisms where the network continuously monitors connection metrics (RSSI, throughput, load) and provides redistribution recommendations to clients. This feedback loop allows the network to optimize resource allocation dynamically while maintaining simple client association procedures, as clients follow network-guided decisions rather than making independent complex choices.
2Stability of the object's composition
If clients remain associated with initial AP, then association stability is high, but load balancing is poor
Solution Approach 1:
The system makes the association dynamic by allowing clients to transition between APs based on changing network conditions. The network monitors connection metrics continuously and triggers redistribution when thresholds are exceeded, enabling the system to maintain stability during normal operation while dynamically adjusting associations when load balancing becomes necessary.
Solution Approach 2:
The system changes key parameters (connection metrics, load thresholds, RSSI values) to determine when redistribution should occur. By monitoring these parameters and comparing them against predefined thresholds, the system can maintain stable associations under normal conditions while triggering rebalancing when performance degradation is detected, thus resolving the contradiction between stability and load balancing.
3Productivity
If redistribution is implemented, then network efficiency improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary redistribution mechanism where the network acts as a mediator between clients and APs. Rather than clients making complex independent decisions or the system implementing complex client-side changes, the network calculates connection metrics and sends simple redistribution recommendations to clients, who then follow these guided decisions. This intermediary approach improves network efficiency while minimizing the complexity burden on individual components.
4Reliability
If probabilistic redistribution is used, then service quality improves, but decision-making complexity increases
Solution Approach 1:
The system implements self-service by having clients autonomously evaluate probabilistic redistribution recommendations based on their own measurements of connection metrics. Rather than requiring complex centralized decision-making or sophisticated client algorithms, the system provides clients with simple criteria and probabilistic guidance, allowing them to make independent redistribution decisions that improve service quality while keeping the overall system relatively simple.
Data Source
AI summary
Systems and methods for instituting redistribution of wireless clients to improve service, comprising via access point (AP), multiple APs acting in concert, and/or a central entity, in connection with a network, allowing a client to associate; deciding that an associated client should be redistributed based on a connection metric, wherein the decision is based on at least one of, a probability calculation, network metric and a threshold comparison; and via the AP, sending the associated client a trigger, wherein the trigger is at least one of, a redistribution request and a disconnect message.