Centralized RAN Traffic Steering for Cluster-Level Load Balancing
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Solution Overview
Problem
Existing mobile operator networks are statically configured, making dynamic and holistic load balancing across multiple cells and radio access technologies (RATs) challenging, leading to inefficient resource allocation and energy consumption.
Innovation Solution
A centralized management entity using a near-real-time RAN Intelligent Controller (RIC) coordinates traffic steering across a cluster of cells, considering load management information and signal strength, enabling flexible resource allocation and proactive rebalancing to optimize network performance and energy savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a statically configured network is used, then device complexity is reduced and ease of operation is improved, but resource allocation efficiency deteriorates and energy consumption increases
Solution Approach 1:
The patent implements dynamic traffic steering that automatically adjusts network resource allocation based on real-time load conditions. The system transitions from static configuration to dynamic adaptation by continuously monitoring cell load metrics and redistributing traffic across available cells, enabling the network to respond proactively to changing conditions without manual reconfiguration.
Solution Approach 2:
The network system performs self-optimization through automated load balancing mechanisms. The traffic steering functionality autonomously evaluates load management information from multiple cells and redistributes traffic without external intervention, allowing the network to self-adjust resource allocation and improve efficiency while reducing operational complexity.
2Productivity
If traditional load balancing is used, then individual cell load is managed, but holistic cluster-level resource optimization is achieved
Solution Approach 1:
The patent merges individual cell load management into a unified cluster-level optimization framework. By aggregating load management information from multiple cells and applying cluster-wide traffic steering, the system achieves holistic resource optimization that considers inter-cell dependencies and enables coordinated resource allocation across the entire network cluster.
Solution Approach 2:
The traffic steering mechanism serves multiple functions simultaneously: it performs individual cell load balancing, cluster-level resource optimization, and proactive rebalancing. This multi-functional approach enables the system to adapt to various network conditions and optimize resources at both cell and cluster levels through a single unified mechanism.
3Loss of time
If reactive load balancing is used, then response time is reduced, but proactive resource optimization is achieved
Solution Approach 1:
The system implements proactive rebalancing that anticipates network congestion before it occurs. By continuously monitoring load trends and predicting future resource demands, the traffic steering mechanism redistributes traffic in advance to prevent cell overload, thereby optimizing resource utilization before performance degradation occurs rather than merely reacting to congestion.
4Adaptability or versatility
If multiple radio access technologies are integrated, then network versatility is improved, but coordination complexity increases
Solution Approach 1:
The traffic steering mechanism is designed to handle multiple radio access technologies (2G, 4G, 5G) through a unified framework. The system evaluates load management information across different RATs and applies coordinated traffic steering that considers inter-RAT relationships, enabling versatile multi-RAT support while managing coordination complexity through standardized procedures.
Data Source
AI summary
A method is disclosed for network agility traffic steering at a centralized management entity in a network system, the method comprising: receiving load management information from a plurality of cells within a network cluster; evaluating the load management information to determine resource allocation for the plurality of cells within the network cluster; generating traffic steering instructions based on the evaluation; transmitting the traffic steering instructions to the cells within the network cluster to dynamically adjust resource allocation and network performance; and enabling clustering of cells through containerized microservices coordinated using Kubernetes, allowing for flexible and scalable deployment of network functions.