STORM: Enabling Traffic Scheduling for RDMA

Simplifying Prioritization and Scheduling with P2CS

Modern datacenter networks host diverse services and workloads with varying quality-of-service (QoS) requirements, yet are constrained by hardware limitations. Most notably, the small number of physical priority queues available in commodity switches. Existing scheduling mechanisms, whether end-host or in-network based, struggle to scale under these constraints due to their reliance on global priority information or complex queue management. This paper presents P2CS (Priority-based Probabilistic Congestion Signaling), a lightweight and scalable approach that enables fine-grained traffic prioritization using only a single FIFO queue. P2CS combines priority-aware probabilistic congestion signaling, priority-aware packet dropping, and simple switch-side arbitration to enforce prioritization across flows. P2CS supports a range of scheduling objectives, and requires minimal software changes making it readily deployable in today's datacenter infrastructure. Evaluation on representative workloads, including multi-tenant ML training, HPC, and mixed spray/ECMP traffic, demonstrates that P2CS achieves performance comparable to in-network mechanisms while significantly reducing complexity and cost.

ProLet: Proactive Multi-path Load Balancing for Lossless RDMA

To achieve high-throughput and low-latency Remote Direct Memory Access (RDMA) communication in data center networks, load balancing is critical for preventing congestion and ensuring that traffic is efficiently distributed across available network paths. However, existing schemes may not effectively detect rerouting opportunities in continuous RDMA packet streams and may degrade in-order delivery, limiting their applicability to RDMA traffic. To address these limitations, we propose ProLet, a load balancing scheme that enables proactive probing and reroutes elephant flows at flowlet granularity in lossless RDMA networks. ProLet dynamically fine-tunes per-destination top-of-rack timeouts and enables effective in-network flowlet identification based on real-time network conditions. Meanwhile, it leverages lightweight mice flows as proactive probes to maintain network-wide congestion awareness. This allows ProLet to reroute elephant flows before congestion accumulates, mitigating the persistent queue buildup inherent in subflow-based schemes. Extensive numerical evaluations demonstrate that ProLet reduces average and tail flow completion time slowdowns by 69% and 79%, respectively, compared to state-of-the-art load balancing schemes.

MemExchange: Utility-Driven Distributed Memory Reallocation for Multi-Tenant Datacenters

To handle unpredictable workloads, cloud providers typically over-provision memory to meet peak demand, resulting in substantial underutilization across datacenter clusters. At the same time, memory-constrained tenants may suffer elevated cache miss rates, even when idle capacity remains stranded elsewhere in the infrastructure. MemExchange is a cluster-wide, multi-tenant memory management system that dynamically right-sizes in-memory caching tenants according to workload demand. Leveraging marginal-utility-based allocation derived from online Miss Ratio Curve (MRC) estimation, MemExchange redistributes idle memory between tenants across physical nodes using RDMA. This approach transforms the dedicated caching memory scattered across servers into a logically aggregated pool, enabling cross-node memory exchange without centralized coordination or forced tenant co-location. To support efficient remote access, we design the MemExchange Tracker Communication (MTC) protocol, an application-layer mechanism that coordinates memory reallocation and enables one-sided RDMA operations without involving remote CPUs. We implement MemExchange in Memcached and evaluate it through microbenchmarks, medium and rack-scale deployments of up to 100 CloudLab servers. Our results show up to 2.3x lower remote-access overhead compared to TCP-based designs, a 13% increase in cluster-wide memory utilization at rack scale, and up to 63% reduction in miss rate for memory-constrained tenants under skewed workloads.

Performance Prediction of Data-Rebalancing Actions in Sharded NoSQL Clusters

Scalable storage systems typically provide mechanisms for re-distributing data over time to ensure a balanced storage allocation. Such mechanisms have traditionally been considered as background activities meant to run at low priority, to avoid penalizing applications accessing storage. In this work, we focus on an alternative design point where data re-distribution is run as a high-priority activity, meant to re-balance data at the full speed allowed by the newly added resources. Such rapid redistribution has become viable in recent years due to the rise of low-overhead technologies in the networking and disk storage space. A challenge in such a scenario is to schedule data transfer flows to newly added nodes in a way that fully and efficiently utilizes network, CPU, and disk resources in the new nodes, while avoiding overload. To understand the impact of different parameters of the rebalance process, such as the number of simultaneous senders, we develop a queueing network (QN) model of the process used in MongoDB, and describe a preliminary evaluation of performance prediction by the QN model via simulations.

ARDA: I/O Scheduler for Heterogeneous Workloads Co-located on Ultra-low-latency SSDs

StrataCL: Fabric-Native Communication Library for Production Supernodes

Modern distributed AI workloads run across hundreds of accelerators, making communication a major bottleneck. Existing communication libraries remain largely buffer-centric because user and communication buffers are managed separately, causing redundant data copies or costly user-buffer registration. This paper presents StrataCL, a zero-redundancy and fabric-native communication library for production supernodes. StrataCL introduces registration-on-allocation to realize user-buffer direct communication, and designs communication operators with workload-balanced NPU-core partitioning and NPU-driven SDMA offloading to exploit supernode architecture features. On the Huawei CloudMatrix384, StrataCL improves collective bus bandwidth by up to 1.6x and improves MoE dispatch/combine bus bandwidth by up to 1.4x. Across three production workloads, StrataCL improves LLM inference throughput by 1.9x, reduces P99 TTFT by 2.2x, and reduces LLM and Recsys training iteration time by 1.4x and 1.3x, respectively.