REPS: Recycled Entropy Packet Spraying for Adaptive Load Balancing and Failure Mitigation

Next-generation datacenters require highly efficient network load balancing to manage the growing scale of artificial intelligence (AI) training and general datacenter traffic. Existing solutions designed for Ethernet, such as Equal Cost Multi-Path (ECMP) and oblivious packet spraying (OPS), struggle to maintain high network utilizations as datacenter topologies (and network failures as a consequence) continue to grow. To address these limitations, we propose REPS, a lightweight decentralized per-packet adaptive load balancing algorithm designed to optimize network utilization while ensuring rapid recovery from link failures. REPS adapts to network conditions by caching good-performing paths. In case of a network failure, REPS re-routes traffic away from it in less than 100 microseconds. REPS is designed to be deployed with next-generation out-of-order transports, such as Ultra Ethernet, and introduces less than 25 bytes of per-connection state. We extensively evaluate REPS in large-scale simulations and FPGA-based NICs.