Next-generation datacenters require highly efficient network load balancing to manage the growing scale of artificial intelligence (AI) training and general datacenter traffic. However, existing Ethernet-based solutions, such as Equal Cost Multi-Path (ECMP) and oblivious packet spraying (OPS), struggle to maintain high network utilization due to both increasing traffic demands and the expanding scale of datacenter topologies, which also exacerbate network failures. 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 uses less than 25 bytes of per-connection state regardless of the topology size. We extensively evaluate REPS in large-scale simulations and FPGA-based NICs.

Many-to-one communication (i.e., incast) is a long-standing challenge in networking with a wide range of proposed solutions. However, as incast-inducing applications today (e.g., storage, ML training) scale beyond a single datacenter, they introduce new challenges that current solutions do not handle. In particular, inter-datacenter links have orders of magnitude higher latency than intra-datacenter paths, lengthening the feedback loop that senders rely on to adjust their sending rates and drastically increasing incast completion times.

Cloud computing and AI workloads are driving unprecedented demand for efficient communication within and across datacenters. However, the coexistence of intra- and inter-datacenter traffic within datacenters plus the disparity between the RTTs of intra- and inter-datacenter networks complicates congestion management and traffic routing. Particularly, faster congestion responses of intra-datacenter traffic causes rate unfairness when competing with slower inter-datacenter flows. Additionally, inter-datacenter messages suffer from slow loss recovery and, thus, require reliability. Existing solutions overlook these challenges and handle inter- and intra-datacenter congestion with separate control loops or at different granularities. We propose Uno, a unified system for both inter- and intra-DC environments that integrates a transport protocol for rapid congestion reaction and fair rate control with a load balancing scheme that combines erasure coding and adaptive routing. Our findings show that Uno significantly improves the completion times of both inter- and intra-DC flows compared to state-of-the-art methods such as Gemini.

Financial exchanges have begun a move from on-premise and custom-engineered datacenters to the public cloud, accelerated by a rush of new investors, the rise of remote work, cost savings from the cloud, and the desire for more resilient infrastructure. While the promise of the cloud is enticing, the cloud's varying network latencies can lead to market unfairness: orders can be processed out of sequence, and market data can be disseminated to market participants at incorrect times due to varying latencies between participants and the exchange. We present CloudEx, a fair-access cloud exchange, which leverages high-precision software clock synchronization to compensate for noisy network conditions in the public cloud. We also discuss refinements to the CloudEx design that were informed by lessons learned from deploying CloudEx in two academic courses and conclude by outlining future research directions.