Vincent Lannurien
d822cdaa6a
4.9 KiB
title, venue, year, date, tags, paper_count, draft
| title | venue | year | date | tags | paper_count | draft |
|---|---|---|---|---|---|---|
| SoCC 2025 Digest | SoCC | 2025 | 2025-01-01 | 13 | false |
13 papers selected.
From Bottleneck to Breakthrough: Optimizing Scheduling for Hyperscale Containerized Clusters
Bing Li, Yuquan Ren, Xinyi Song, Zhilei Liu et al.
TL;DR — Documents production-scale scheduling improvements at a hyperscale cloud provider, demonstrating how targeted optimizations reduce scheduling tail latency and increase cluster utilization in real containerized workloads.
CPU-Limits kill Performance: Time to rethink Resource Control
Chirag C. Shetty, Sarthak Chakraborty, Hubertus Franke, Larisa Shwartz et al.
TL;DR — Challenges the conventional use of CPU cgroup limits in cloud environments, showing through production evidence that CFS bandwidth throttling degrades application QoS and proposing a rethink of resource control abstractions.
Rethinking Tiered Memory Management in Cloud Data Centers
Tong Xing 0002, Jiaxun Yang, Javier Picorel, Antonio Barbalace
TL;DR — Proposes a novel tiered memory management framework for cloud data centers that improves performance by rethinking the placement and migration policies across DRAM and CXL/NVM tiers.
Cost-Efficient Cloud Infrastructure with Hugepage-aware Memory Deduplication
Ruizhe Huang, Xinyu Wang 0043, Zhida An, Hanwen Lei et al.
TL;DR — Deploys hugepage-aware memory deduplication in a large production cloud, achieving significant memory savings without the performance regressions that plague conventional THP-based deduplication.
ALAP: Intent-Based Serverless Computing via Delayed Decision-Making
Prasoon Sinha, Kostis Kaffes, Neeraja J. Yadwadkar
TL;DR — Introduces an intent-based programming model for serverless that defers scheduling decisions until runtime context is available, improving resource efficiency and SLO attainment over eager placement strategies.
Hydra: Virtualized Multi-Language Runtime for High-Density Serverless Platforms
Serhii Ivanenko, Vasyl Lanko, Rudi Horn, Vojin Jovanovic et al.
TL;DR — Presents a virtualized runtime that multiplexes multiple language environments within a single sandbox, enabling higher function density and faster cold starts on serverless platforms.
Serverless Elasticsearch: the Architecture Transformation from Stateful to Stateless
Iraklis Psaroudakis, Pooya Salehi, Jason Bryan, Francisco Fernández Castaño et al.
TL;DR — Describes Elastic's production migration of Elasticsearch to a serverless, stateless architecture, sharing engineering lessons on decoupling compute from state at cloud scale.
DFUSE: Strongly Consistent Write-Back Kernel Caching for Distributed Userspace File Systems
Haoyu Li, Jingkai Fu, Qing Li 0002, Windsor Hsu et al.
TL;DR — Closes a long-standing gap in FUSE-based distributed file systems by enabling strongly consistent write-back caching in the kernel, significantly improving throughput without sacrificing correctness.
Accelerating Distributed Filesystem Metadata Service via Decoupling Directory Semantics from Metadata Indexing
Wenhao Lv, Hao Guo, Qing Wang 0031, Youyou Lu et al.
TL;DR — Achieves scalable distributed filesystem metadata by separating directory namespace semantics from the underlying index structure, reducing contention and improving throughput for large-scale cloud storage.
Valet: Efficient Data Placement on Modern SSDs
Devashish R. Purandare, Peter Alvaro, Avani Wildani, Darrell D. E. Long et al.
TL;DR — Exploits fine-grained internal SSD geometry to make smarter data placement decisions, yielding measurable I/O performance gains without changes to the host storage stack.
Understanding Diffusion Model Serving in Production: A Top-Down Analysis of Workload, Scheduling, and Resource Efficiency
Yanying Lin, Shuaipeng Wu, Shutian Luo, Hong Xu 0001 et al.
TL;DR — Provides the first comprehensive production characterization of diffusion model inference workloads, revealing unique scheduling and resource efficiency challenges distinct from LLM serving.
ModServe: Modality- and Stage-Aware Resource Disaggregation for Scalable Multimodal Model Serving
Haoran Qiu, Anish Biswas, Zihan Zhao, Jayashree Mohan et al.
TL;DR — Disaggregates compute resources per modality and pipeline stage for multimodal inference, with a Microsoft production deployment showing improved GPU utilization and latency over monolithic serving.
THORN-ML: Transparent Hardware Offloaded Resilient Networks for RDMA based Distributed ML Workloads
Maziyar Nazari, Daniel Noland, Giulio Sidoretti, Erika Hunhoff et al.
TL;DR — Offloads RDMA fault detection and recovery to programmable network hardware, making distributed ML training resilient to network failures without modifying the training framework or incurring software overhead.