Cloud Controller Network Congestion Steering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud-based Wi-Fi networks face congestion issues due to saturated radio channels, leading to inefficient service for client devices, which existing technologies fail to manage effectively without disrupting user experience.
Innovation Solution
A cloud controller and edge controller system that monitors network performance metrics, identifies congested areas, and steers clients to less congested radio bands or access points using load balancing and band steering techniques, with the cloud controller sending steering commands to the edge controller to dynamically reallocate resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clients are served by congested radio channels, then network coverage is maintained, but network performance degrades due to saturation
Solution Approach 1:
The system changes the operating parameters of client devices by steering them from congested radio channels to less congested ones. The cloud controller monitors congestion metrics (channel utilization, signal strength, client count) and dynamically adjusts client-radio associations by sending steering commands to edge controllers, which then guide clients to alternative access points or frequency bands, thereby resolving the contradiction between maintaining coverage and preserving performance
Solution Approach 2:
The cloud controller acts as an intermediary between edge controllers and client devices to manage congestion. It collects performance metrics from edge controllers, analyzes congestion conditions, and sends steering commands to edge controllers which then implement the steering of clients. This intermediary coordination enables centralized optimization of network resource allocation without direct client intervention
2Productivity
If cloud controller centrally manages all steering decisions, then congestion optimization is improved, but system complexity and latency increase
Solution Approach 1:
The system segments the congestion management function into two parts: the cloud controller performs centralized analysis and decision-making based on aggregated network metrics, while edge controllers execute local steering operations. This segmentation allows the cloud controller to focus on optimization algorithms without handling individual client steering operations, reducing overall system complexity while maintaining effective congestion management
Solution Approach 2:
The edge controller serves as an intermediary that receives steering commands from the cloud controller and translates them into client-specific actions. It buffers and processes commands locally, reducing the communication overhead between the cloud controller and numerous client devices, thereby managing system complexity while preserving centralized optimization capabilities
3Productivity
If clients are steered to less congested bands, then network efficiency is improved, but user experience may be disrupted
Solution Approach 1:
The system performs preliminary assessments before steering clients by evaluating multiple criteria including signal strength thresholds, client capabilities (dual-band support), and current connection quality. Only clients meeting specific conditions are selected for steering, and target radios are pre-validated to ensure adequate signal quality. This preliminary filtering ensures that steering operations improve network efficiency while maintaining acceptable user experience
Solution Approach 2:
The system implements feedback mechanisms where edge controllers monitor steering command outcomes and report success rates to the cloud controller. The cloud controller uses this feedback to adjust steering strategies, avoiding repeated steering attempts for clients with poor steering success rates or those experiencing connection issues. This feedback loop ensures that steering operations continuously improve network efficiency while adapting to maintain user experience
Data Source
AI summary
Various embodiments relate to a cloud controller configured to control a wireless network having an edge controller, including: a network interface configured to communicate with the wireless network; a memory; a processor coupled to the memory and the network interface, wherein the processor is further configured to: receive performance metrics for the wireless network including congestion information; compare the congestion information with congestion criteria; identify clients connected to the wireless network as candidates to be transferred; sending a steering command to the edge controller with a list of identified clients to steer; and receiving an indication of clients steered by the edge controller.