Dynamic Load Balancing
The traditional data center load balancing uses hash-based methods to keep packets of the same flow on a single path. However, this approach ignores real-time link bandwidth usage, which can cause uneven network resource utilization. As a result, this method can lead to imbalanced traffic distribution and congestion—issues that are especially critical for Artificial Intelligence (AI) and Machine Learning (ML) workloads, which frequently involve large data transfers that heavily strain network bandwidth.
The Dynamic Load Balancing (DLB) feature advances conventional hash-based load balancing by incorporating various strategies that optimize traffic distribution across members of an Equal-Cost Multi-Path (ECMP) group.
Unlike static hash-based approaches, which allocate flows without factoring in real-time link conditions, DLB continuously monitors and adjusts traffic patterns.
This strategy improves performance and link utilization during dynamic traffic allocation:
• The system allocates new data flows based on the real-time load of each Equal-Cost Multi-Path (ECMP) member.
• When load conditions change, it reassigns existing flows to different paths while maintaining flow integrity and avoiding packet reordering.
DLB ensures proper bandwidth utilization among group members. A large flow on a single link will not disrupt other smaller flows in the ECMP group..