Elastic Wireless Control Plane Scaling Access Points
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Solution Overview
Problem
Conventional wireless control plane (WCP) systems fail to differentiate between idle and peak times, leading to unnecessary costs and power usage due to constant operation of WCP instances, even during minimal network load, and do not efficiently scale with varying client demands.
Innovation Solution
Implement an elastic wireless control plane system that partitions access points into active and sleep mode groups based on evaluated load factors, dynamically determining and adjusting the number of WCP instances needed, with periodic re-evaluation and activation according to schedules or changes in demand, using hysteresis to prevent frequent mode transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If WCP instances operate continuously to process AP status reports and keep-alive signals, then network reliability is maintained, but energy consumption and hosting costs increase during idle periods
Solution Approach 1:
The patent implements dynamic scaling of WCP instances based on network load conditions. During peak hours, multiple WCP instances operate to handle high AP and client loads. During off-peak hours, the system automatically reduces the number of active WCP instances while maintaining essential functionality through reduced monitoring intervals and adjusted timeout values, thus resolving the contradiction between reliability and energy consumption.
Solution Approach 2:
The system changes operational parameters such as monitoring intervals, timeout values, and WCP instance counts based on time-of-day patterns and network demand. By adjusting these parameters dynamically, the system maintains network reliability during peak times while significantly reducing energy consumption during idle periods when network activity is minimal.
2Device complexity
If the same number of WCP instances run during idle and peak times, then system simplicity is maintained, but hosting costs and power usage increase wastefully
Solution Approach 1:
The system transitions from static to dynamic operation by automatically adjusting the number of active WCP instances based on real-time network conditions. A controller monitors network parameters and triggers scaling actions to match WCP instance count with actual demand, eliminating wasteful energy usage during off-peak hours while maintaining simplicity through automated decision-making.
Solution Approach 2:
The system implements feedback mechanisms where the controller continuously monitors network load, AP status, and client connectivity. Based on this feedback, the system automatically adjusts WCP instance allocation, ensuring optimal resource utilization without manual intervention and preventing wasteful energy consumption during low-demand periods.
3Use of energy by moving object
If WCP instances are scaled down during off-peak hours, then energy consumption and costs are reduced, but response time to network changes may increase
Solution Approach 1:
The system dynamically adjusts WCP instance counts while maintaining the ability to quickly scale up when needed. During off-peak hours, fewer instances operate with extended monitoring intervals. When network conditions change, the system can rapidly activate additional WCP instances or wake sleeping instances from reduced monitoring mode, ensuring acceptable response times are maintained despite reduced baseline capacity.
Solution Approach 2:
The system keeps WCP instances in a ready state with adjusted timeout values and monitoring intervals during off-peak hours. When network demand increases, these pre-configured instances can quickly resume full operation without complete cold startup delays, balancing energy savings with rapid response capability through preliminary system preparation.
Data Source
AI summary
One or more implementations can include methods, systems and computer readable media for elastic wireless control planes. In some implementations, the method can include evaluating one or more elastic wireless control plane mode factors, and partitioning each of a plurality of access points into one of an active mode group and a sleep mode group based on the evaluating. The method can also include determining a number of wireless control plane instances needed based on the partitioning. The method can further include activating the number of wireless control plane instances and shutting down any excess wireless control plane instances beyond the number.