Vincent Lannurien
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Markdown
117 lines
4.9 KiB
Markdown
---
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title: SoCC 2025 Digest
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venue: SoCC
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year: 2025
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date: '2025-01-01'
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tags: []
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paper_count: 13
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draft: false
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---
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13 papers selected.
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### From Bottleneck to Breakthrough: Optimizing Scheduling for Hyperscale Containerized Clusters
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*Bing Li, Yuquan Ren, Xinyi Song, Zhilei Liu *et al.**
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**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.
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### CPU-Limits kill Performance: Time to rethink Resource Control
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*Chirag C. Shetty, Sarthak Chakraborty, Hubertus Franke, Larisa Shwartz *et al.**
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**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.
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### Rethinking Tiered Memory Management in Cloud Data Centers
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*Tong Xing 0002, Jiaxun Yang, Javier Picorel, Antonio Barbalace*
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**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.
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### Cost-Efficient Cloud Infrastructure with Hugepage-aware Memory Deduplication
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*Ruizhe Huang, Xinyu Wang 0043, Zhida An, Hanwen Lei *et al.**
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**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.
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### ALAP: Intent-Based Serverless Computing via Delayed Decision-Making
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*Prasoon Sinha, Kostis Kaffes, Neeraja J. Yadwadkar*
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**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.
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### Hydra: Virtualized Multi-Language Runtime for High-Density Serverless Platforms
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*Serhii Ivanenko, Vasyl Lanko, Rudi Horn, Vojin Jovanovic *et al.**
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**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.
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### Serverless Elasticsearch: the Architecture Transformation from Stateful to Stateless
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*Iraklis Psaroudakis, Pooya Salehi, Jason Bryan, Francisco Fernández Castaño *et al.**
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**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.
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### DFUSE: Strongly Consistent Write-Back Kernel Caching for Distributed Userspace File Systems
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*Haoyu Li, Jingkai Fu, Qing Li 0002, Windsor Hsu *et al.**
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**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.
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### Accelerating Distributed Filesystem Metadata Service via Decoupling Directory Semantics from Metadata Indexing
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*Wenhao Lv, Hao Guo, Qing Wang 0031, Youyou Lu *et al.**
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**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.
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### Valet: Efficient Data Placement on Modern SSDs
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*Devashish R. Purandare, Peter Alvaro, Avani Wildani, Darrell D. E. Long *et al.**
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**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.
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### Understanding Diffusion Model Serving in Production: A Top-Down Analysis of Workload, Scheduling, and Resource Efficiency
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*Yanying Lin, Shuaipeng Wu, Shutian Luo, Hong Xu 0001 *et al.**
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**TL;DR** — Provides the first comprehensive production characterization of diffusion model inference workloads, revealing unique scheduling and resource efficiency challenges distinct from LLM serving.
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### ModServe: Modality- and Stage-Aware Resource Disaggregation for Scalable Multimodal Model Serving
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*Haoran Qiu, Anish Biswas, Zihan Zhao, Jayashree Mohan *et al.**
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**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.
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### THORN-ML: Transparent Hardware Offloaded Resilient Networks for RDMA based Distributed ML Workloads
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*Maziyar Nazari, Daniel Noland, Giulio Sidoretti, Erika Hunhoff *et al.**
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**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.
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