Unlocking AI Performance: A Deep Dive Into Arista's Cluster Load Balancing (CLB)

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Frequently Asked Questions - FAQs

Artificial Intelligence has changed the stakes for data center networks. In AI environments, Job Completion Time (JCT) is the critical metric, because when multi-million-dollar GPU clusters sit idle waiting for data, businesses lose both time and ROI.

Lossless Ethernet fabrics and high-speed links are foundational for AI networking. Yet even with robust hardware, a significant hidden challenge can choke performance: inefficient load balancing of AI traffic flows.

The AI Traffic Jam: Why Traditional Load Balancing Falls Short

AI and machine learning workloads generate east-west traffic patterns that look nothing like typical enterprise data flows.

Instead of numerous small, transient flows, AI training and inference jobs rely on a small number of large, long-lived flows—so-called “elephant flows.” These flows typically occur between GPUs during collective operations, such as AllReduce and model synchronization.

Here’s the problem with Traditional Load Balancing (ECMP):

Works by hashing packet header fields (source IP, destination IP, L4 ports) to distribute flows across multiple equal-cost paths.

Effective for diverse, high-entropy traffic (e.g., web or transactional workloads).

Fails for AI workloads because AI elephant flows often have identical header fields, resulting in hash collisions.

When multiple elephant flows get hashed to the same physical link, while other links remain idle, it causes:

Network hotspots: Localized oversubscription on specific links.

Incast congestion:Occurs when multiple senders target a single receiver simultaneously, thereby overwhelming the buffer capacity.

High tail latency: The critical issue in AI networks, where a few delayed packets stall the entire synchronous training process.

In synchronous AI workloads, the entire GPU cluster must wait for the slowest operation to finish. A single congested path can turn a fast training job into a multi-hour slog.

Moreover, traditional load balancing is usually unidirectional. It only optimizes the path from leaf-to-spine, neglecting the equally critical spine-to-leaf return path. This asymmetry leaves additional performance on the table and fails to address bidirectional congestion dynamics.

How Arista’s Cluster Load Balancing (CLB) Solves the Problem

Arista’s Cluster Load Balancing (CLB) was built precisely to address these AI-specific networking pain points. It’s part of the Arista EOS® Smart AI Suite and operates with two core innovations:

RDMA-Aware Flow Placement

Instead of relying on basic L3/L4 header fields, CLB leverages the unique characteristics of RDMA (Remote Direct Memory Access) traffic.

RDMA Queue Pairs (QPs):

RDMA connections are identified by QPs, which provide more granular flow differentiation than IP and TCP/UDP alone.

A single AI collective operation may generate numerous QPs, offering additional entropy for load distribution.

CLB uses these QP identifiers to intelligently split elephant flows across multiple physical paths. This:

Dramatically reduces hash collisions.

Maximizes path utilization.

Avoids hotspots even for flows with otherwise identical headers.

This RDMA-aware mechanism is crucial because traditional load balancers see elephant flows as indistinguishable single flows, whereas CLB sees them as multiple granular flows that can be intelligently distributed.

Global, Bidirectional Optimization

Conventional load balancing solutions only consider individual packet flows in isolation and usually in one direction. CLB takes a fabric-wide view:

Bidirectional Optimization:

Balances traffic from leaf-to-spine and spine-to-leaf.

Prevents congestion not just on outbound paths but also on return traffic, which is critical in synchronous AI training.

Fabric-Wide Awareness:

Monitors global utilization across all spine paths.

Dynamically adjusts flow placement based on real-time congestion metrics.

By optimizing both directions and monitoring the entire topology, CLB ensures traffic is evenly distributed throughout the entire network fabric. This eliminates bottlenecks and keeps latency consistent even under heavy AI loads.

Quantifying CLB’s Advantage

Arista’s internal testing and field deployments show the tangible benefits of CLB:

Traditional dynamic load balancing (DLB) systems achieve ~90% utilization efficiency.

Networks deployed with CLB consistently reach 98.3% efficiency.

This translates to:

8-10% higher throughput on existing network links.

Faster Job Completion Times (JCT).

Lower tail latency, which prevents GPU idle cycles.

Direct cost savings and higher ROI from expensive AI infrastructure.

In large AI clusters, the network often accounts for 20-30% of the total runtime of training jobs. CLB’s gains mean significant reductions in time-to-results for AI workloads.

Winning the Battle Against Tail Latency

High tail latency is the silent killer of AI efficiency. One congested link or a burst of packet drops can stall thousands of GPUs, leaving expensive resources idle.

Arista’s CLB directly attacks this problem by:

Avoiding hash collisions.

Evenly distributing elephant flows across multiple physical links.

Maintaining predictable, low latency across all paths.

Leading hyperscale operators have validated CLB’s effectiveness. Jag Brar, Vice President and Distinguished Engineer at Oracle Cloud Infrastructure (OCI), confirms:

“As Oracle continues to grow its AI infrastructure leveraging Arista switches, we see a need for advanced load balancing techniques to help avoid flow contentions and increase throughput in ML networks. Arista’s Cluster Load Balancing feature helps do that.”

When paired with CloudVision Universal Network Observability (CV UNO), enterprises gain visibility into:

Real-time congestion hotspots.

Flow-level telemetry tied to specific AI jobs.

Direct correlations between network health and Job Completion Time (JCT).

This observability enables proactive tuning and rapid troubleshooting before minor issues escalate into major delays.

The Intelligent Visibility Advantage

While CLB is a breakthrough technology, deploying it effectively is not plug-and-play. To extract its full performance potential, enterprises must:

This is where Intelligent Visibility adds crucial value. As an expert Arista partner, Intelligent Visibility offers:

Expert-Led Network Design:

Customized architectures for AI workloads.

Optimized RoCEv2 deployments.

Selection of appropriate buffer sizes and port speeds.

Precision Deployment and Tuning:

Advanced configuration of CLB and congestion controls.

Integration with telemetry platforms for real-time visibility.

Co-Managed Operations (Aegis Services):

Continuous monitoring of flow efficiency.

Proactive anomaly detection and rapid troubleshooting.

Automation of updates and configuration compliance.

By partnering with Intelligent Visibility, enterprises bridge the gap between advanced Arista technology and reliable business outcomes, ensuring that every dollar invested in AI hardware delivers maximum returns.

Ready to Unlock the Power of CLB?

AI infrastructure isn’t just about raw compute. It’s about ensuring that data moves swiftly and predictably across the network fabric. Arista’s Cluster Load Balancing is a critical innovation for achieving this goal.

Connect with Intelligent Visibility today to explore how we can help architect, deploy, and operate your AI network for peak performance and efficiency.