Consensus, widely regarded as the most fundamental primitive in distributed systems, lies at the core of countless services that require coordination among remote processes. Datacenter services typically achieve consensus through long-established, quorum-based algorithms such as Paxos and Raft, including recent re-adaptations for kernel bypass datapaths (e.g. smartNIC/RDMA-based consensus). While these optimizations can reduce latency to the mu s-scale, they remain constrained by inherent message complexity, namely the need for acknowledgments from majority quorums to tolerate faults and arbitrary message delays. Our approach takes a step further from bare acceleration of classical primitives, focusing instead on leveraging FPGA-smartNIC and priority-queue reservation to achieve synchronous remote interactions in practice. We use synchrony to devise a novel, efficient quorum-less consensus protocol which we use to build Nano-consensus: a novel hardware consensus engine. Nano-consensus operates at network line rate and can reach consensus in 1.03 mu s for single-packet instances, delivering 3.82x latency and 4.8x improvements over the state of the art. We demonstrate how Nano-consensus can be integrated into distributed applications to boost both performance and consistency.
S3-compatible object storage has become ubiquitous, used by an ever-expanding range of applications. Workload traces show that many of these applications treat object storage like a traditional file system, with many small reads and writes, yet object storage implementations have not kept up. Optimized for bulk storage, these systems cannot efficiently exploit modern SSDs, requiring large hardware installations to achieve operation rates typical of local file systems on modest machines. ZStore is a highly-efficient object store designed for modern hardware, providing strong consistency (per-key linearizability) via a novel architecture which replicates data over independent per-device shared logs, using NVMe-over-Fabrics as its backend storage protocol. Based on a 3-node symmetric active-active cell, ZStore performs small reads and writes with minimal I/O amplification (beyond replication factor) while supporting object sizes up to the S3 maximum of 5 TB and optional erasure coding for objects larger than 128 KB. ZStore guarantees single-key linearizability using a two-phase coordination mechanism, tracking in-flight writes so that reads of stable data can be handled on a single gateway, with a heavier-weight multi-node read protocol used only when interfering writes are detected. Our evaluation shows ZStore achieving nearly an order of magnitude improvement in IOPS over widely-used systems (MinIO and Ceph) when evaluated on comparable hardware.
Machine Learning (ML) emerged as a powerful tool for predicting task execution times across the variety of VM types offered by Infrastructure-as-a-Service (IaaS) clouds. However, training ML models to ensure accurate predictions can often become uneconomical for users due to the high costs-in terms of both time and money-for collecting samples, especially when an IaaS cloud offers a wide choice of VM types. This paper investigates a ML model boot-strapping technique that leverages analytical modeling to reduce the cost of collecting training samples while maintaining robust performance predictions. Complementarily, the technique can be used to improve the accuracy of ML models in the case of limited availability of training samples. Experimental results highlighted the potential of the proposed technique with various workloads and with a large set of VM types, paving the way for more cost-effective ML-based performance prediction in IaaS clouds.
Synchronous consensus protocols are often considered impractical due to their reliance on strict timing assumptions. In this paper, we revisit this perception with a focus on public cloud environments, where infrastructure has evolved to offer increasingly predictable network behavior. We report on an extensive empirical study across major cloud providers and regions to evaluate whether the timing guarantees required by synchronous protocols hold in practice. Our measurements show that bounded communication delays are not only common but also stable across a variety of configurations when messages are small (i.e., <= 4 KB). Motivated by these observations, we explore the design space of robust synchronous consensus protocols suited for deployment in public clouds. We introduce SyncPaxos, a synchronous version of the celebrated Paxos protocol, designed for partially synchronous environments, and several variants that benefit from the characteristics of public cloud environments. We analyze their performance and resilience to timing violations and identify the conditions under which they remain safe and performant. Our findings suggest that synchrony is not a theoretical relic but a viable and efficient foundation for building resilient distributed systems in today's cloud infrastructure.
Small deep learning models deployed at edge servers suffer from decreasing accuracy due to data drift and hence require continual learning, which leads to resource competition with inference execution, decreasing accuracy and latency service-level-objective (SLO) fulfillment. Previous methods fail to maximize both accuracy and SLO fulfillment. To address this problem, we propose a Checkpointed Inference-based system for accurate and SLO-guaranteed data drift-resilient model Serving (CIS). CIS incorporates checkpointed inference - maximizing accuracy by continuously switching to intermediately retrained models during inference. However, model switching introduces significant inference latency overhead. To mitigate this, first, CIS proposes a lightweight allocation and placement scheduler to minimize switching time. Second, during inference execution, CIS reorders retraining samples to reduce switching frequency. Third, it temporarily reallocates GPU space from retraining tasks to inference tasks to address request queuing issue caused by the switching, with minimal impact on accuracy. Trace-driven experiments show that CIS achieves up to 25.1% higher accuracy without affecting SLO fulfillment, and requires 4x lower GPU cost compared to the existing method in achieving similar or higher accuracy.
