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1. Chipmunk: Investigating Crash-Consistency in Persistent-Memory File Systems

   https://doi.org/10.1145/3552326.3567498  

   LeBlanc, Hayley; Pailoor, Shankara; K R E, Om Saran; Dillig, Isil; Bornholt, James; Chidambaram, Vijay (May 2023, ACM)

   We present Chipmunk, a new framework to test persistent-memory (PM) file systems for crash-consistency bugs. Using Chipmunk, we discovered 23 new bugs across five PM file systems; most bugs have been confirmed and fixed by developers. The discovered bugs have serious consequences, including making the file system un-mountable or breaking rename atomicity. We present a detailed study of the bugs found using Chipmunk and discuss important lessons learned for designing and testing PM file systems. 

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2. Understanding and dealing with hard faults in persistent memory systems

   https://doi.org/10.1145/3447786.3456252  

   Choi, Brian; Burns, Randal; Huang, Peng (April 2021, Proceedings of the Sixteenth European Conference on Computer Systems)

   null (Ed.)

   The advent of Persistent Memory (PM) devices enables systems to actively persist information at low costs, including program state traditionally in volatile memory. However, this trend poses a reliability challenge in which multiple classes of soft faults that go away after restart in traditional systems turn into hard (recurring) faults in PM systems. In this paper, we first characterize this rising problem with an empirical study of 28 real-world bugs. We analyze how they cause hard faults in PM systems. We then propose Arthas, a tool to effectively recover PM systems from hard faults. Arthas checkpoints PM states via fine-grained versioning and uses program slicing of fault instructions to revert problematic PM states to good versions. We evaluate Arthas on 12 real-world hard faults from five large PM systems. Arthas successfully recovers the systems for all cases while discarding 10× less data on average compared to state-of-the-art checkpoint-rollback solutions. 

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3. SplitFS: Reducing Software Overhead in File Systems for Persistent Memory

   https://doi.org/10.1145/3341301.3359631  

   Rohan Kadekodi, Se Kwon (October 2020, Proceedings of the 27th ACM Symposium on Operating Systems Principles)

   We present SplitFS, a file system for persistent memory (PM) that reduces software overhead significantly compared to state-of-the-art PM file systems. SplitFS presents a novel split of responsibilities between a user-space library file system and an existing kernel PM file system. The user-space library file system handles data operations by intercepting POSIX calls, memory-mapping the underlying file, and serving the read and overwrites using processor loads and stores. Metadata operations are handled by the kernel PM file system (ext4 DAX). SplitFS introduces a new primitive termed relink to efficiently support file appends and atomic data operations. SplitFS provides three consistency modes, which different applications can choose from, without interfering with each other. SplitFS reduces software overhead by up-to 4× compared to the NOVA PM file system, and 17× compared to ext4 DAX. On a number of micro-benchmarks and applications such as the LevelDB key-value store running the YCSB benchmark, SplitFS increases application performance by up to 2× compared to ext4 DAX and NOVA while providing similar consistency guarantees. 

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4. Concolic Testing of Front-end JavaScript

   Li, Zhe; Xie, Fei (April 2023, International Conference on Fundamental Approaches to Software Engineering (FASE))

   JavaScript has become the most popular programming language for web front-end development. With such popularity, there is a great demand for thorough testing of client-side JavaScript web applications. In this paper, we present a novel approach to concolic testing of front-end JavaScript web applications. This approach leverages widely used JavaScript testing frameworks such as Jest and Puppeteer and conducts concolic execution on JavaScript functions in web applications for unit testing. The seamless integration of concolic testing with these testing frameworks allows injection of symbolic variables within the native execution context of a JavaScript web function and precise capture of concrete execution traces of the function under test. Such concise execution traces greatly improve the effectiveness and efficiency of the subsequent symbolic analysis for test generation. We have implemented our approach on Jest and Puppeteer. The application of our Jest implementation on Metamask, one of the most popular Crypto wallets, has uncovered 3 bugs and 1 test suite improvement, whose bug reports have all been accepted by Metamask developers on Github. We also applied our Puppeteer implementation to 21 Github projects and detected 4 bugs. 

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5. WineFS: a hugepage-aware file system for persistent memory that ages gracefully

   https://doi.org/10.1145/3477132.3483567  

   Rohan Kadekodi, Saurabh Kadekodi (October 2021, Proceedings of the Symposium on Operating Systems Principles)

   Modern persistent-memory (PM) file systems perform well in benchmark settings, when the file system is freshly created and empty. But after being aged by usage, as will be the normal mode in practice, their memory-mapped performance degrades significantly. This paper shows that the cause is their inability to use 2MB hugepages to map files when aged, having to use 4KB pages instead and suffering many extra page faults and TLB misses as a result. We introduce WineFS, a novel hugepage-aware PM file system that largely eliminates this effect. WineFS combines a new alignment-aware allocator with fragmentation-avoiding approaches to consistency and concurrency to preserve the ability to use hugepages. Experiments show that WineFS resists the effects of aging and outperforms state-of-the-art PM file systems in both aged and un-aged settings. For example, in an aged setup, the LMDB memory-mapped database obtains 2× higher write throughput on WineFS compared to NOVA, and 70% higher throughput compared to ext4-DAX. When reading a memory-mapped persistent radix tree, WineFS results in 56% lower median latency than NOVA. 

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