Aug 2026· Proceedings of the ACM SIGCOMM 2026 Conference· 0 citations· 66 references
TL;DR
Spillway introduces a DPU-host hybrid data plane that repurposes idle host CPU resources to process spillover traffic when the DPU becomes the bottleneck, and decouples virtual switching capacity from static DPU hardware limits.
Abstract
The transition to Data Processing Unit (DPU)-centric architectures has become the de-facto standard in modern cloud networks, enabling infrastructure offload and improved host resource utilization. However, the fixed hardware limits of DPUs increasingly fail to keep pace with the rapid growth of host compute density and network-intensive workloads. As a result, when DPU resources are saturated, host compute capacity often remains underutilized due to insufficient network provisioning. This paper presents Spillway, a unified vSwitching fabric that decouples virtual switching capacity from static DPU hardware limits. Spillway introduces a DPU-host hybrid data plane that repurposes idle host CPU resources to process spillover traffic when the DPU becomes the bottleneck. Unlike traditional software switching, Spillway activates host-based forwarding selectively through a unified resource scheduler, incurring no overhead under normal operating conditions. To ensure production viability, Spillway enforces strict performance isolation across heterogeneous data planes. Experimental results from a large-scale deployment show that, under bursty traffic workloads, Spillway improves single-node connection processing capacity by up to 90% with negligible interference to co-located applications.
Erasure coding provides efficient fault tolerance for large-scale distributed storage systems. However, its data repair process is well-known to be resource-intensive. We find that conventional host-centric, TCP-based repair architectures suffer from severe resource contention. Even in high-bandwidth networks, such interference slows repair operations and degrades service quality. To address this limitation, we propose DPUEC, a novel architecture that fully offloads the entire repair process to a Data Processing Unit (DPU). DPUEC leverages Remote Direct Memory Access (RDMA) to enable kernel-bypass data transfers and utilizes the onboard multi-core processors of DPU for highly parallel decoding. This design establishes a physically isolated data plane for repair traffic, completely bypassing host resources. Our prototype evaluation shows that DPUEC improves repair throughput by 133% - 181% over the state-of-the-art approach across different workloads. More importantly, DPUEC delivers true performance isolation. It eliminates any interference with foreground applications and reduces host CPU utilization on the recovery node to nearly zero.
XFir is the first hardware-accelerated new-flow setup system for cloud host servers that delivers high CPS throughput while preserving sufficient flexibility, and is cost-effective to deploy, requiring only a single DPU per host.
Shihan Lin, Shunqiao Jiang, Liang Wang et al.· Proceedings of the ACM SIGCO...· 1 citation
Compute and memory resources in cloud environments are strictly managed and isolated by the control plane; in contrast, network resources lack equivalent management and isolation mechanisms. This best-effort treatment of networking leads to significant challenges for modern AI workloads, which have diverse and bandwidth-intensive communication patterns. Without fine-grained network resource control, these workloads suffer from interference, unpredictable throughput, and suboptimal cluster utilization. To address these issues, this paper demonstrates how network bandwidth can be elevated to a first-class, schedulable, and enforceable resource within Kubernetes, the de facto standard for cloud-native orchestration. We introduce a new scheduling capability that models network interfaces as allocatable resources and regulates bandwidth sharing through the Dynamic Resource Allocation (DRA) framework, with enforcement implemented using the Hierarchical Token Bucket (HTB) mechanism. We evaluate the system using multitenant AI workloads derived from real-world communication characteristics with a simulation-based approach and validate the proposed enforcement strategy in a real cluster. Results show that the proposed two-level bandwidth allocation improves tenant performance predictability and satisfaction while maintaining packed cluster utilization.
To alleviate the overhead of moving massive data in modern data centers, Data Processing Units (DPUs) are increasingly deployed to offload NVMe-over-Fabrics (NVMeoF) remote storage access from the CPU. However, current heterogeneous CPU-DPU systems suffer from inflexible resource partitioning, leading to suboptimal utilization of computational power and network bandwidth. In this paper, we present DPIO, a unified I/O processing stack designed to harmonize the collaboration between CPU and DPU in NVMeoF environments. By implementing fine-grained task orchestration, DPIO effectively bridges the performance gap between DPU offloading and CPU processing, achieving near-optimal system performance across diverse workloads. Our real-world implementation demonstrates that DPIO delivers over 2 × higher throughput compared to conventional DPU-centric NVMeoF approaches, significantly enhancing the efficiency of data-intensive applications.
Wenhao Gu, Xuchao Xie, Yujuan Tan et al.· IEEE International Symposium...· 0 citations
The experience in designing, deploying, and operating Pegasus, a data center network tailored for the AI cloud, along with operational lessons learned from its deployment are shared.
Xianneng Zou, Yadong Liu, Yiran Zhang et al.· Proceedings of the ACM SIGCO...· 0 citations