# CS 591 Sys-Net [![hackmd-github-sync-badge](https://hackmd.io/RnBY_-2BSSmSsNYsOmIG7A/badge)](https://hackmd.io/RnBY_-2BSSmSsNYsOmIG7A) ## Week2: Some interesting paper from ATC/OSDI 2022 - Xudong Sun ### Automatic Reliability Testing For Cluster Management Controllers Fuzz test ### DuoAI Ivy vs CoQ - What is the advantages of Ivy compared to other verification language like CoQ? Because I have heard of CoQ but Ivy is new to me. ### RESIN: Memory Leak #### Previous Tech - static - dynamic 人工智障 Fair idea - 首先内存如果慢慢上涨,那多半有问题。其次,在其他机器上运行没问题,那一定是你的问题。 - 误报率有点高 ### Debugging the OmniTable Way Idea: replay Insight: Lazy materialization >SteamDrill introduces lazy materialization as a solution. Rather than materializing an OmniTable during execution, SteamDrill uses deterministic record and replay [10] to cap- ture a log of non-deterministic inputs to the execution. The system uses the log to generate OmniTable state on-demand by instrumenting and re-executing the original execution as necessary to resolve debugging queries. Delaying OmniTable materialization allows SteamDrill to filter OmniTable data before extracting state instead of afterwards. ## Week4: OSDI / ATC ## Week 5: VLDB Very Large DataBases - Engines - Graphs - ML,AI 4 Papers Today ### Netherite: Efficient Execution of Serverless Workflows Write Buffer ### DBOS: a DBMS-oriented operating system Distributed OS by DBMS everything is a file -> everything is a table Database Operating System - straw - rude test - wood - run one app - brick - finally scheduler: context switch file system: page change, pointers, metadata, e.g. scheduler: FIFO with sql: \`order by\` 和几年前那个 excel 操作系统有的一拼 ![](https://i.imgur.com/aBZaqTk.png) ### S￿￿￿￿￿: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks Its common to use Graph database to do training, such as use nebura for GNN. The innovation of this paper should focus on how to avoid communication. The techniques this paper used are “Despite the promising performance, the major challenge that limits the adoption of GNNs to large-scale graphs lies in the inability to utilize all data in ￿nite time and the scalability of the algorithm itself.” ([Peng 等。, 2022, p. 1937](zotero://select/library/items/QVR4CWIF)) ([pdf](zotero://open-pdf/library/items/U43KUQTT?page=1&annotation=K5ZBHVC8))