# SIGCOMM 2025 ## Meta Info Homepage: ### Paper List * * ### Acceptance Rate 16% (= 74 / 460 (approx.)) ## Papers ### Large Language Models (LLMs) * Infrastructure * InfiniteHBD: Building Datacenter-Scale High-Bandwidth Domain for LLM with Optical Circuit Switching Transceivers \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750468)] \[[Video](https://www.youtube.com/watch?v=d4PX36vVDX0)] * PKU & StepFun & Lightelligence * Key insight: unify connectivity and dynamic switching at the transceiver level using OCS. * Realize the transceiver-centric HBD architecture in production โ†’ Flexible construction of arbitrarily large ring topologies & improved system resilience * Silicon Photonics (SiPh) based OCS transceiver (OCSTrx) * Reconfigurable k-hop ring topology โ†’ Each node connects to all other nodes within โ‰ค๐พ hops via OCSTrx * HBD-DCN orchestration algorithm โ†’ Minimize cross-ToR traffic * Astral: A Datacenter Infrastructure for Large Language Model Training at Scale \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750521)] \[[Video](https://www.youtube.com/watch?v=Ou389tkXL1I)] * NJU & Tencent * LLM Training * DistTrain: Addressing Model and Data Heterogeneity with Disaggregated Training for Multimodal Large Language Models \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750472)] \[[Video](https://www.youtube.com/watch?v=O_qdjBsLwE8)] \[[arXiv](https://arxiv.org/abs/2408.04275)] * PKU & StepFun * Disaggregated model orchestration: separate the training for modality encoder (ViT for images, Beats for audios), LLM backbone, and modality generator (Diffusion for images, AudioLDM for audio). * Disaggregated data preprocessing: decouple data preprocessing from training. * Integrated with Megatron-LM. * ByteScale: Communication-Efficient Scaling of LLM Training with a 2048K Context Length on 16384 GPUs \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3754352)] \[[Video](https://www.youtube.com/watch?v=cXsMyc7ROyo)] * PKU & ByteDance * Limitations of existing works * The mismatch between data heterogeneity & static mesh โ†’ Redundant communication and imbalanced computation. * **HDP**: Hybrid Data Parallelism * Unify the inter- and intra-data partitioning with a dynamic mesh design. * A communication optimizer * Eliminate the redundant communication for *short sequences* by *data-aware sharding* and *dynamic communication*. * Compress the communication cost for *long sequences* by *selective offloading*. * A balance scheduler โ†’ Mitigate the imbalanced computation by *parallelism-aware data assignment*. * From ATOP to ZCube: Automated Topology Optimization Pipeline and A Highly Cost-Effective Network Topology for Large Model Training \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750503)] \[[Video](https://www.youtube.com/watch?v=7fVx3b1T_dc)] * THU & Zhongguancun Laboratory & Harnets.AI & ByteDance * ATOP: Automated Topology Optimization Pipeline * Model network topology as a set of hyperparameters โ†’ Enable the discovery of potential network topologies. * A new topology ZCube, discoverd by ATOP. * Reach the highest cost-effectiveness across various GPU scale. * SkeletonHunter: Diagnosing and Localizing Network Failures in Containerized Large Model Training \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750513)] \[[Video](https://www.youtube.com/watch?v=s1hPuZ033Nk)] * THU & Alibaba Cloud * Key idea: reason about the traffic skeleton, which comprises a crucial set of network paths consistently traversed by the training traffic. * Privacy-preseving LLM Inference * SCX: Stateless KV-Cache Encoding for Cloud-Scale Confidential Transformer Serving \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750509)] \[[Video](https://www.youtube.com/watch?v=isix2nPNkyI)] \[[Code](https://github.com/yuanmu97/scx)] * CUHK * Encode the intermediate key-value cache using user-controlled keys โ†’ Ensure that the cloud can neither recover the input nor independently complete the next token prediction. * LLMOps * Intent-Driven Network Management with Multi-Agent LLMs: The Confucius Framework \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750537)] \[[Video](https://www.youtube.com/watch?v=QpCXjseK0HQ)] * Meta * Model network management workflows as DAGs to aid planning. * Integrate LLMs with existing *management tools* to achieve seamless operational integration, employ RAG to improve long-term memory, and establish a set of primitives to systematically support *human/model interaction*. * Integrate with existing *network validation* methods and incorporate its own validation framework to prevent regressions. * Towards LLM-Based Failure Localization in Production-Scale Networks \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750505)] \[[Video](https://www.youtube.com/watch?v=95NQmZrTwbk)] * NJU & Alibaba Cloud * BiAn (็‹ด็Šด), an LLM-based framework for efficient incident investigation. * Process monitoring data and generate error device rankings with detailed explanations. ### Mixture-of-Experts (MoEs) * MoE Training * MixNet: A Runtime Reconfigurable Optical-Electrical Fabric for Distributed Mixture-of-Experts Training \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750465)] \[[Video](https://www.youtube.com/watch?v=zYVtGsWzAjI)] * HKUST * Design and implement a regionally reconfigurable HBD that augments existing electrical interconnects using OCS. * MoE Inference * MegaScale-Infer: Efficient Mixture-of-Experts Model Serving with Disaggregated Expert Parallelism \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750506)] \[[Video](https://www.youtube.com/watch?v=eXHC8OpI2hk)] \[[arXiv](https://arxiv.org/abs/2504.02263)] * PKU & ByteDance * Attention/FFN Disaggregation (AFD) * Provide a M2N communication library โ†’ Eliminate unnecessary GPU-to-CPU data copies, group initialization overhead, and GPU synchronization. ### RDMA * Reliability * Revisiting RDMA Reliability for Lossy Fabrics \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750480)] \[[Video](https://www.youtube.com/watch?v=4CgsjKMJ1Ns)] * HKUST & Huawei * **Best Student Paper Award (Honorable Mention)** * DCP co-designs the switch and RNICs, including DCP-Switch and DCP-RNIC. * Header-only-based retransmission. * Bitmap-free packet tracking. * Prototype DCP-Switch using P4 switch and DCP-RNIC using FPGA. * Virtualization * Software-based Live Migration for RDMA \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750487)] \[[Video](https://www.youtube.com/watch?v=_JTZhc6wYxo)] * THU & MSRA * MigrRDMA: a software-based RDMA live migration. * Provide a software indirection layer to achieve transparent switching to new RDMA communications. * Implemented over Mellanox RNICs. * ByteDance Jakiro: Enabling RDMA and TCP over Virtual Private Cloud \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750496)] \[[Video](https://www.youtube.com/watch?v=gh13ILZGY1s)] * ByteDance * Support fundamental VPC features (e.g., QoS, security groups) for both RDMA and TCP streams. * Alibaba Stellar: A New Generation RDMA Network for Cloud AI \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750539)] \[[Video](https://dl.acm.org/doi/10.1145/3718958.3750539)] * Alibaba Cloud * Limitations of existing RDMA virtualization solutions (e.g., SR-IOV) * Host-level * The number of VFs is static โ†’ Cannot dynamically scale the number of VFs. * The container must pin all of its memory in the host memory before initiating any RDMA operation โ†’ A minute-level start-up delay. * PCIe-level * LUT in PCIe fabrics is severely limited in size โ†’ Only a small number of VFs to enable GDR. * RNIC-level * No support for strict isolation between RDMA and non-RDMA traffic. * Three designs * Para-Virtualized Direct Memory Access (**PVDMA**) for on-demand memory pinning โ†’ Reduce host memory consumption & mitigate the start-up delay of secure containers. * Extended Memory Translation Table (**eMTT**) for optimized GDR performance โ†’ Allow the RNIC to bypass unnecessary consultations of memory address mappings in the PCIe fabric. * RDMA Packet Spray for efficient multi-path utilization * Performance Diagnosis * Hawkeye: Diagnosing RDMA Network Performance Anomalies with PFC Provenance \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750490)] \[[Video](https://www.youtube.com/watch?v=wp5h3cFMXsk)] * THU & BUAA & Infrawaves * Three designs * A PFC-aware telemetry mechanism โ†’ Record the PFC impact on flows * An in-network PFC causality analysis and tracing mechanism โ†’ Collect causal telemetry for diagnosis * A provenance-based diagnosis algorithm โ†’ Present the anomaly breakdown, identify the anomaly type and root causes * Evaluated on both NS-3 simulations and a Tofino testbed. * I/O Acceleration * CEIO: A Cache-Efficient Network I/O Architecture for NIC-CPU Data Paths \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750488)] \[[Video](https://www.youtube.com/watch?v=yMJG4m56-eo)] \[[Code](https://github.com/axio-project/ceio)] * HKUST * Limitations of traditional I/O acceleration strategies (e.g., Data Direct I/O (DDIO), RDMA) * Inefficient utilization of the LLC. * Cache-efficient I/O โ†’ Line-rate throughput and ยตs-scale tail latency * Limit I/O Rate โ†’ Proactive rate control * Limit I/O Capacity โ†’ Elastic buffer * Implemented on commodity SmartNICs and incorporated into DPDK and RDMA libraries. ### Hardware Transport * Falcon: A Reliable, Low Latency Hardware Transport \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3754353)] \[[Video](https://www.youtube.com/watch?v=nu18DsuvnlU)] * Google * Support multiple Upper Layer Protocols (ULPs) and heterogeneous application workloads in general-purpose Ethernet datacenter environments (with losses and without special switch support). * Key designs: delay-based congestion control with multipath load balancing, a layered design with a simple request-response transaction interface for multi-ULP support, hardware-based retransmissions and error-handling for scalability, a programmable engine for flexibility. * ```

Falcon hardware transport layers

``` ### Collective Communication * ResCCL: Resource-Efficient Scheduling for Collective Communication \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750514)] \[[Video](https://www.youtube.com/watch?v=uu594-CfWNE)] * NEU & SIAT, CAS & Alibaba Cloud * Limitations of existing works (e.g., NCCL, RCCL, MSCCL) * Static resource allocation and scheduling mechanisms โ†’ Inefficient utilization of bandwidth and SM resources for various collective algorithms * Three designs * Optimize scheduling at the primitive level (e.g., send and recvReduceCopy). * Enable flexible thread block allocation. * Generate lightweight communication kernels to minimize runtime overhead. * SyCCL: Exploiting Symmetry for Efficient Collective Communication Scheduling \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750499)] \[[Video](https://www.youtube.com/watch?v=x1vL9SbcZmE)] * Alibaba Cloud & THU * Limitations of existing works * Existing collective communication libraries (e.g., NCCL, RCCL) rely on fixed schedules and cannot adjust to varying topology and model requirements. * Existing collective schedule synthesizers (e.g., TECCL, TACCL) utilize Mixed Integer Linear Program for modeling but encounter scalability challenges. * SyCCL, a scalable collective schedule synthesizer โ†’ Synthesize near-optimal schedules in tens of minute. * Leverage collective and topology symmetries to decompose the original collective communication demand into smaller sub-demands within smaller topology subsets. * Propose efficient search strategies to explore potential sub-demands, synthesizes corresponding sub-schedules, and integrates these sub-schedules into complete schedules. ### Video Streaming * Towards User-level QoE: Large-scale Practice in Personalized Optimization of Adaptive Video Streaming \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750526)] \[[Video](https://www.youtube.com/watch?v=vWqnKwmlkwQ)] * THU & Kuaishou & SFU * **LingXi**, a system for personalized adaptive video streaming. * Dynamically optimize the objectives of adaptive video streaming algorithms by analyzing user engagement. * Iteratively determine optimal parameters through Monte Carlo sampling and online Bayesian optimization. * TLadder: QoE-Centric Video Ladder Optimization with Playback Feedback at Billion Scale \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750500)] \[[Video](https://www.youtube.com/watch?v=pKPHFzR3Cww)] * ByteDance * Jointly consider the video content dimension (i.e., the bitrate-quality tradeoff of candidate representations) and the playback feedback dimension (e.g., network condition, rebuffering time, and playback bitrate). * ACE: Sending Burstiness Control for High-Quality Real-time Communication \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750520)] \[[Video](https://www.youtube.com/watch?v=_c8fOxVZsAU)] * HKUST & ByteDance * A dual-control approach that manages both the encoding and transmission burstiness. * Sender: dynamically adjust the bucket size of a token-based pacer to control burstiness at the granularity of frame level. * Encoder: an adaptive complexity mechanism that smoothens frame sizes without sacrificing quality. * Harnessing WebRTC for Large-Scale Live Streaming \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750535)] \[[Video](https://www.youtube.com/watch?v=Bf_zTLZ2C5w)] * ByteDance * Focus on optimizing first-frame delay, startup video rebuffering, audio-to-video drift, and per-session CPU usage. * Scalable Video Conferencing Using SDN Principles \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750489)] \[[Video](https://www.youtube.com/watch?v=_8v9ZTrFkZI)] \[[Code](https://github.com/princeton-cabernet/scallop)] * Princeton & UVA * **Scallop**, an SDN-inspired SFU (Selective Forwarding Unit) * Decouple video-conferencing applications into a hardware-based data plane for latency-sensitive and frequent media operations. * A software control plane for the (infrequent) remaining tasks (e.g., analyze feedback signals, session management). ### CXL * Understanding and Profiling CXL.mem Using PathFinder \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750479)] \[[Video](https://www.youtube.com/watch?v=PHqrdRGRfD4)] \[[Code](https://github.com/netlab-wisconsin/PathFinder)] * UW-Madison & BUAA & Intel * Leverage the capabilities of existing PMUs and dissect the `CXL.mem` protocol at adequate granularities. * Key idea: view the server processor and its chipset as a multi-stage Clos network, equip each architectural module with a PMU-based telemetry engine, track different `CXL.mem` paths, and apply conventional traffic analysis techniques. * Perform snapshot-based path-driven profiling and introduce four techniques: path construction, stall cycle breakdown, interference analyzer, and cross-snapshot analysis. * Built atop Linux Perf. ### Network Failures * SkyNet: Analyzing Alert Flooding from Severe Network Failures in Large Cloud Infrastructures \[[Paper](https://dl.acm.org/doi/10.1145/3718958.3750536)] \[[Video](https://www.youtube.com/watch?v=3NhCnKqgBtM)] * Alibaba Cloud * Extract scope and severity information from alert floods. * Integrate multiple monitoring data sources through a uniform input format. ## Acronyms * RDMA: Remote Direct Memory Access * OCS: Optical Circuit Switching * HBD: High-Bandwidth Domain * DCN: Datacenter Network * ToR: Top-of-Rack * VPC: Virtual Private Cloud * SR-IOV: Single-Root Input/Output Virtualization * GDR: GPUDirect RDMA * VF: Virtual Function * LUT: Look-Up Table * LLC: Last-Level Cache * CXL: Compute Express Link * PMU: Performance Monitoring Unit * WebRTC: Web Real-Time Communications * DAG: Directed Acyclic Graph * RAG: Retrieval-Augmented Generation