How AI Improves Load Balancing to Cut p95/p99 Latency

Round-robin load balancing, often the default algorithm used in distributed systems, can inadvertently cause incidents because it does not account for the heterogeneous nature of real-world environments, where partial degradations, or "gray failures," often occur. These degradations aren't reflected in traditional health checks, leading to increased tail latency and retries, which can manifest as application bugs. Modern load balancers now serve as traffic control planes, optimizing for user outcomes by minimizing tail latency, preventing overload spirals, and reducing the blast radius of failures. They incorporate advanced features such as adaptive retries, slow-start for new instances, and traffic shaping, which are essential in preventing minor issues from escalating into major outages. AI can enhance these systems by predicting saturation and anomalies, enabling preemptive traffic steering, and thereby improving resilience and reducing latency. Edge-based load balancing, such as that offered by Azion, enhances reliability by making real-time routing decisions closer to users, allowing for faster detection and response to degradation, which ultimately improves business metrics like conversion rates.

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