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venue: SoCC
year: 2025
papers:
- title: 'From Bottleneck to Breakthrough: Optimizing Scheduling for Hyperscale Containerized
Clusters'
authors:
- Bing Li
- Yuquan Ren
- Xinyi Song
- Zhilei Liu
- Cong Xu
- Jingyuan Zhang
- Caixue Lin
- Wu Xiang
- Rui Shi
reason: "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."
- title: 'CPU-Limits kill Performance: Time to rethink Resource Control'
authors:
- Chirag C. Shetty
- Sarthak Chakraborty
- Hubertus Franke
- Larisa Shwartz
- Chandra Narayanaswami
- Indranil Gupta
- Saurabh Jha
reason: "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."
- title: Rethinking Tiered Memory Management in Cloud Data Centers
authors:
- Tong Xing 0002
- Jiaxun Yang
- Javier Picorel
- Antonio Barbalace
reason: "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."
- title: Cost-Efficient Cloud Infrastructure with Hugepage-aware Memory Deduplication
authors:
- Ruizhe Huang
- Xinyu Wang 0043
- Zhida An
- Hanwen Lei
- Peng Jiang 0007
- Ziqi Zhang
- Ding Li 0001
- Yao Guo 0001
- Xiangqun Chen
- Yuntao Liu
- Kang Zhou
- Yuxin Ren 0001
- Ning Jia 0004
- Xinwei Hu
reason: "Deploys hugepage-aware memory deduplication in a large production cloud, achieving significant memory savings without the performance regressions that plague conventional THP-based deduplication."
- title: 'ALAP: Intent-Based Serverless Computing via Delayed Decision-Making'
authors:
- Prasoon Sinha
- Kostis Kaffes
- Neeraja J. Yadwadkar
reason: "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."
- title: 'Hydra: Virtualized Multi-Language Runtime for High-Density Serverless Platforms'
authors:
- Serhii Ivanenko
- Vasyl Lanko
- Rudi Horn
- Vojin Jovanovic
- Rodrigo Bruno
reason: "Presents a virtualized runtime that multiplexes multiple language environments within a single sandbox, enabling higher function density and faster cold starts on serverless platforms."
- title: 'Serverless Elasticsearch: the Architecture Transformation from Stateful to
Stateless'
authors:
- Iraklis Psaroudakis
- Pooya Salehi
- Jason Bryan
- Francisco Fernández Castaño
- Brendan Cully
- Ankita Kumar
- Henning Andersen
- Thomas Repantis
reason: "Describes Elastic's production migration of Elasticsearch to a serverless, stateless architecture, sharing engineering lessons on decoupling compute from state at cloud scale."
- title: 'DFUSE: Strongly Consistent Write-Back Kernel Caching for Distributed Userspace
File Systems'
authors:
- Haoyu Li
- Jingkai Fu
- Qing Li 0002
- Windsor Hsu
- Asaf Cidon
reason: "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."
- title: Accelerating Distributed Filesystem Metadata Service via Decoupling Directory
Semantics from Metadata Indexing
authors:
- Wenhao Lv
- Hao Guo
- Qing Wang 0031
- Youyou Lu
- Jiwu Shu
reason: "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."
- title: 'Valet: Efficient Data Placement on Modern SSDs'
authors:
- Devashish R. Purandare
- Peter Alvaro
- Avani Wildani
- Darrell D. E. Long
- Ethan L. Miller
reason: "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."
- title: 'Understanding Diffusion Model Serving in Production: A Top-Down Analysis
of Workload, Scheduling, and Resource Efficiency'
authors:
- Yanying Lin
- Shuaipeng Wu
- Shutian Luo
- Hong Xu 0001
- Haiying Shen
- Chong Ma
- Min Shen
- Le Chen
- Chengzhong Xu 0001
- Lin Qu
- Kejiang Ye
reason: "Provides the first comprehensive production characterization of diffusion model inference workloads, revealing unique scheduling and resource efficiency challenges distinct from LLM serving."
- title: 'ModServe: Modality- and Stage-Aware Resource Disaggregation for Scalable
Multimodal Model Serving'
authors:
- Haoran Qiu
- Anish Biswas
- Zihan Zhao
- Jayashree Mohan
- Alind Khare
- Esha Choukse
- Íñigo Goiri
- Zeyu Zhang 0005
- Haiying Shen
- Chetan Bansal
- Ramachandran Ramjee
- Rodrigo Fonseca
reason: "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."
- title: 'THORN-ML: Transparent Hardware Offloaded Resilient Networks for RDMA based
Distributed ML Workloads'
authors:
- Maziyar Nazari
- Daniel Noland
- Giulio Sidoretti
- Erika Hunhoff
- Tamara Silbergleit Lehman
- Eric Keller
reason: "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."