Topology-Aware Load Balancer for Network Bandwidth Optimization
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Solution Overview
Problem
Traditional load balancing strategies fail to effectively utilize network bandwidth resources in large-scale data centers due to a lack of topology awareness, leading to suboptimal resource planning and manual adjustments, which are error-prone and costly.
Innovation Solution
A topology-aware load balancing method that acquires configuration information to determine backend service endpoints' locations, calculates priorities based on topology information, health statuses, and link quality, and adjusts service response endpoints dynamically to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional load balancing strategies are used without topology awareness, then the system is simple to implement, but network bandwidth resource utilization is poor
Solution Approach 1:
The patent implements a feedback mechanism where the load balancer continuously collects topology information from backend service endpoints and link quality metrics, then uses this feedback to dynamically adjust load balancing decisions. This closed-loop feedback system enables the balancer to adapt to changing network conditions and optimize bandwidth utilization without manual intervention.
Solution Approach 2:
The load balancing system transitions from static, pre-configured rules to dynamic, real-time adjustments based on current topology and link quality. The balancer continuously polls for updates and recalculates routing decisions, making the system adaptive to changing network conditions while maintaining operational simplicity through automated control.
2Reliability
If manual adjustments are made for load balancing, then the implementation is straightforward, but errors are frequent and costs are high
Solution Approach 1:
The load balancer operates autonomously by automatically collecting topology information, assessing link quality, and making routing decisions without human intervention. The system self-manages the complexity of load balancing optimization, eliminating manual configuration errors while maintaining high reliability through continuous adaptive adjustment based on real-time network conditions.
3Productivity
If topology information is not collected, then the system operates quickly with minimal overhead, but resource planning is suboptimal
Solution Approach 1:
The load balancer integrates multiple functions into a single system: it acts as both the load distribution controller and the topology information collector. By combining these functions, the system avoids the overhead of separate monitoring infrastructure while enabling optimized resource planning through comprehensive topology awareness and link quality assessment.
4Reliability
If dynamic adjustment based on topology changes is implemented, then user experience is improved, but computational overhead increases
Solution Approach 1:
The load balancer implements periodic polling of topology information and link quality metrics at scheduled intervals rather than continuously monitoring all parameters. This periodic action reduces computational overhead by processing updates only when changes occur, while still maintaining reliable service quality through timely detection and response to network condition changes.
Data Source
AI summary
A topology-aware load balancing method includes: acquiring load balancing configuration information, determining, based on the configuration information, whether a plurality of backend service endpoints for load balancing are located on different nodes; in response to the backend service endpoints for load balancing being located on different nodes, for each node, regularly issuing a command for polling the backend service endpoints on the node, acquiring topology information of the different nodes, as well as health statuses and a link quality of the backend service endpoints; calculating priorities of the backend service endpoints based on the topology information, the health statuses and the link quality, configuring a service response endpoint for load balancing based on the priorities; in response to at least one of the topology information, the health statuses and the link quality being changed, recalculating priorities of the backend service endpoints, adjusting the service response endpoint based on the priorities.