Fast consensus protocols, such as CURP-Q and NOPaxos, have coupled objectives of reducing latency and tolerating faults, but doing so incurs considerable processing overhead and/or necessitates complex changes to existing architectures. Our insight is that when no adverse execution conditions (including operation conflicts, node failures, and network failures) occur, replication can be done safely without consensus at all. Rather than pursuing one single consensus protocol for fast and fault-tolerant replication, we propose to decouple low latency from fault tolerance, and design a hybrid solution that seamlessly integrates client-coordinated two-phase commit (2PC) with consensus, respectively to reduce latency in normal situations and tolerate faults in faulty situations. In the absence of faults, the system enters the Fast mode, where the client directly broadcasts its requests to all replicas with 2PC completing all updates in the first phase, i.e., in one RTT. Otherwise, the system enters the Consensus mode, which resorts to a consensus protocol to tolerate faults. We have applied the hybrid solution to the widely-used Raft consensus protocol to design xRaft, which can adaptively switch between the Fast and Consensus modes, ensuring correctness with minimal switching overhead. Evaluation shows that xRaft significantly outperforms state-of-the-art consensus protocols.

The rapidly-increasing computing power of GPUs has rendered the I/O subsystem a bottleneck for distributed deep learning (DL) training. Currently, substantial data preprocessing work (e.g., decoding) has to be conducted on CPUs for a wide range of training scenarios such as computer vision (CV) and audio. Unfortunately, the involvement of training nodes' host memory and/or CPUs on the critical path of loading data to GPUs incurs significant GPU stalls in modern RDMA training clusters, because CPUs are much slower than GPUs and the connection from PCIe switches to host memory tends to suffer from incast problems. Moreover, this also incurs high CPU usage and resource contention, which consequently causes data loading performance variation and stragglers. This paper presents KSpeed, a novel data provisioning framework for large-scale RDMA training clusters. As many data preprocessing tasks need to be done by CPUs, KSpeed organizes host memory and CPU resources in the cluster to build a disaggregated memory/CPU pool, where the nodes can read raw input data from backend storage to their host memory, preprocess the data by their CPUs if necessary, and write cached/preprocessed data (on demand) directly to the training workers' GPU memory to minimize GPU stalls. KSpeed leverages the multi-rail RDMA network to eliminate unnecessary memory copies, interference, and congestion. Evaluation on a 96-GPU cluster shows that KSpeed delivers$5.4 \times \sim 100 \times$higher data loading performance over the state-of-the-art designs (DPP and Alluxio). KSpeed achieves near-linear scalability as the GPU number increases from 8 to 512.

Byzantine fault tolerant (BFT) consensus protocols are becoming an appealing solution to blockchains. As most blockchain systems are deployed on Wide Area Networks (WANs), with each node acting on behalf of its entity, partially synchronous BFT protocols that rely on network synchrony to elect a single leader can be ill-suited. In contrast, asynchronous protocols have no such timing assumptions. Existing asynchronous protocols confront challenges in terms of both flexibility and performance.