Cloud Client Steering for Mesh Wi-Fi Load Balancing
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Solution Overview
Problem
Existing wireless communication networks face challenges in efficiently determining which access point to connect to when multiple options are available, leading to suboptimal network performance.
Innovation Solution
A Cloud-Server optimizes network performance by collecting, analyzing, and processing data from client steering daemons (CSDs) to determine network parameters that improve client and network performance, using steering policies and mechanisms to steer clients to the most suitable access points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wireless device connects to multiple access points when available, then connection options increase, but network performance optimization becomes difficult
Solution Approach 1:
The patent introduces a cloud server as an intermediary that centralizes the complex task of network performance optimization. The cloud server collects data from multiple access points and wireless devices, performs centralized analysis and optimization calculations, then sends steering commands back to the access points. This mediator approach resolves the contradiction by keeping connection options diverse while offloading optimization complexity to the cloud.
Solution Approach 2:
The patent extracts the optimization logic from individual access points and wireless devices, moving it to a centralized cloud server. By taking out the complex optimization algorithms from the distributed network elements, the system maintains multiple connection options locally while centralizing the decision-making complexity, thus resolving the technical contradiction.
2Productivity
If client steering is implemented without centralized optimization, then local decision-making is simple, but overall network performance is suboptimal
Solution Approach 1:
The patent moves the optimization function from the horizontal dimension (distributed across individual devices) to the vertical dimension (centralized cloud server). This dimensional shift allows comprehensive network-wide optimization without increasing complexity at the device level, as the cloud server operates in a separate architectural layer that aggregates data from all network elements.
3Speed
If access points make independent steering decisions, then local response time is fast, but coordinated network optimization is poor
Solution Approach 1:
The cloud server performs preliminary optimization calculations and prepares steering policies in advance based on aggregated network data. When local conditions change, access points can quickly execute pre-computed steering decisions or receive updated policies from the cloud, combining the benefits of centralized optimization with fast local response times.
Data Source
AI summary
Methods, systems, and apparatus' for optimizing network performance are described herein. A Cloud-Server may optimize one or more mesh networks of a plurality of 802.11 access points (APs) and stations (STA) connected to the APs. The Cloud-Server may work with a client steering daemon (CSD) running on each AP. The Cloud-Server may operate in a location that is remote to the AP(s). The Cloud-Server may collect, store, and process network and client related data from one or more CSDs. The processing may include measuring network (e.g., premises) and client (e.g., STA) performance as well as analyzing (e.g., machine learning, etc.) to determine network parameters that will optimize network performance The Cloud-Server may then apply these network parameters to the relevant CSDs in order to improve per client and per network performance.