Vincent Lannurien
d822cdaa6a
4.4 KiB
title, venue, year, date, tags, paper_count, draft
| title | venue | year | date | tags | paper_count | draft |
|---|---|---|---|---|---|---|
| EuroSys 2024 Digest | EuroSys | 2024 | 2024-01-01 | 13 | false |
13 papers selected.
Pronghorn: Effective Checkpoint Orchestration for Serverless Hot-Starts
Sumer Kohli, Shreyas Kharbanda, Rodrigo Bruno, João Carreira et al.
TL;DR — Demonstrates how carefully orchestrated checkpointing can eliminate cold-start latency in serverless runtimes, achieving near-instant hot-starts with negligible overhead.
Serialization/Deserialization-free State Transfer in Serverless Workflows
Fangming Lu, Xingda Wei, Zhuobin Huang, Rong Chen 0001 et al.
TL;DR — Eliminates the dominant serialization cost in serverless function chaining by enabling direct in-memory state passing, yielding large end-to-end latency reductions.
SplitFT: Fault Tolerance for Disaggregated Datacenters via Remote Memory Logging
Xuhao Luo, Ramnatthan Alagappan, Aishwarya Ganesan
TL;DR — Proposes a principled fault-tolerance design for disaggregated datacenters that exploits the new memory-compute split to recover from failures with low overhead.
Puddles: Application-Independent Recovery and Location-Independent Data for Persistent Memory
Suyash Mahar, Mingyao Shen, TJ Smith, Joseph Izraelevitz et al.
TL;DR — Provides transparent crash consistency and data relocation for persistent memory without requiring application changes, easing adoption of PM-backed storage.
Enoki: High Velocity Linux Kernel Scheduler Development
Samantha Miller, Anirudh Kumar, Tanay Vakharia, Ang Chen 0001 et al.
TL;DR — Enables safe, rapid iteration on Linux CPU schedulers by isolating scheduling logic in user space while keeping kernel integration, dramatically lowering the development barrier.
Transparent Multicore Scaling of Single-Threaded Network Functions
Lei Yan 0003, Yueyang Pan, Diyu Zhou, George Candea et al.
TL;DR — Automatically parallelizes unmodified single-threaded network functions across cores with correctness guarantees, delivering near-linear throughput scaling without code changes.
Hoda: a High-performance Open vSwitch Dataplane with Multiple Specialized Data Paths
Heng Pan, Peng He 0003, Zhenyu Li 0001, Pan Zhang et al.
TL;DR — Redesigns the Open vSwitch dataplane with multiple specialized fast paths, achieving significant throughput improvements for cloud virtual networking.
SmartNIC Security Isolation in the Cloud with S-NIC
Yang Zhou 0008, Mark Wilkening, James Mickens, Minlan Yu
TL;DR — Introduces hardware-enforced security isolation for SmartNIC offload tasks in multi-tenant clouds, preventing cross-tenant attacks without sacrificing offload performance.
Finding Correctness Bugs in eBPF Verifier with Structured and Sanitized Program
Hao Sun 0021, Yiru Xu, Jianzhong Liu, Yuheng Shen et al.
TL;DR — Systematically uncovers verifier logic bugs that can silently allow unsafe eBPF programs to execute in the kernel, with a structured fuzzing approach validated on the Linux eBPF verifier.
CSAL: the Next-Gen Local Disks for the Cloud
Yanbo Zhou, Erci Xu, Li Zhang, Kapil Karkra et al.
TL;DR — Describes a production cloud local-disk system that replaces raw NVMe with a software-defined layer to deliver better performance, reliability, and operational flexibility at hyperscale.
Volley: Accelerating Write-Read Orders in Disaggregated Storage
Shaoxun Zeng, Xiaojian Liao, Hao Guo, Youyou Lu
TL;DR — Addresses write-read ordering hazards in disaggregated storage by pipelining acknowledgements with read-side tracking, substantially reducing tail latency without weakening durability.
Erlang: Application-Aware Autoscaling for Cloud Microservices
Vighnesh Sachidananda, Anirudh Sivaraman
TL;DR — Leverages application-level call-graph semantics to make autoscaling decisions that are both faster and more accurate than black-box reactive policies, cutting SLO violations in microservice deployments.
Automatic Root Cause Analysis via Large Language Models for Cloud Incidents
Yinfang Chen, Huaibing Xie, Minghua Ma, Yu Kang 0006 et al.
TL;DR — Demonstrates an LLM-driven pipeline for automated cloud-incident root cause analysis deployed at Microsoft Azure, showing strong recall and significant reduction in on-call engineer effort.