Dynamic Wireless Node Scaling for Load Optimization
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Solution Overview
Problem
The reliance on cloud computing resources for managing cellular network communications leads to underutilization or overutilization of resources, resulting in inefficiencies and potential slowdowns in the network.
Innovation Solution
Implementing a system that dynamically scales the number of network function nodes based on load thresholds, adding new nodes when existing ones reach high load thresholds and removing nodes when they fall below low load thresholds, thereby optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud computing resources are dedicated for the cellular network, then network management functions are improved, but resource utilization efficiency deteriorates due to underutilization or overutilization
Solution Approach 1:
The patent implements dynamic scaling of network function nodes by adjusting the number of nodes based on real-time load conditions. When load increases, nodes are added; when load decreases, nodes are removed. This dynamic adaptation allows the system to maintain reliable network management while optimizing resource utilization efficiency, preventing both underutilization and overutilization of cloud computing resources.
2Productivity
If the number of network function nodes is increased to handle high load, then network capacity is improved, but resource waste occurs when nodes are underutilized
Solution Approach 1:
The system dynamically adjusts the number of active network function nodes based on real-time load monitoring. When load is high, additional nodes are provisioned to maintain network capacity and performance. When load decreases, nodes are de-provisioned or placed in standby mode, preventing resource waste from underutilized nodes. This dynamic approach ensures network capacity is maintained when needed while optimizing resource efficiency during low-demand periods.
Solution Approach 2:
The patent changes the operational parameters of the network by adjusting the number of active nodes based on load thresholds. High load triggers node addition, while low load triggers node removal or standby placement. This parameter change strategy allows the system to adapt resource allocation to actual demand, preventing both capacity shortages and resource waste.
3Loss of energy
If the number of nodes is dynamically adjusted based on load, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors load conditions on network function nodes and automatically adjusts the number of active nodes accordingly. Load thresholds trigger automatic scaling decisions, with the system adding nodes when load is high and removing or standby-ing nodes when load is low. This closed-loop feedback control improves resource utilization while managing system complexity through automated decision-making based on predefined criteria.
Data Source
AI summary
Systems and method for dynamically scaling network function nodes or pods in a wireless network. A number of existing nodes are determined for the wireless network, and an arrival load rate is determined for the existing nodes. A load existence time is also determined for the existing nodes. High and low load thresholds are defined based on the number of existing nodes, the load existence time, and the arrival load rate. A new node is added to the existing nodes in response to determining that a current load on each existing node meets the high load threshold. And at least one existing node is set for removal in response to determining that the current load on the at least one existing node is below the low load threshold. A new received load is then scheduled to be assigned to an existing node having a least average load.