Argosy: verifying layered storage systems with recovery refinement
Name
3314221.3314585.pdf
Description
Published version
Size
422.08 KB
Format
Adobe PDF
Checksum (MD5)
5cfae8d27bda9e1c92edf2dd56cdc613
Author(s)
Chajed, Tej
Tassarotti, Joseph
Kaashoek, M. Frans
Zeldovich, Nickolai
Date Issued
June 2019
Journal
Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation
Publisher
Association for Computing Machinery (ACM)
Citation
Chajed, Tej et al. "Argosy: verifying layered storage systems with recovery refinement." Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, June 2019, Phoenix, Arizona, Association for Computing Machinery, June 2019. © 2019 owner/author(s)
Version
Final published version
Abstract
Storage systems make persistence guarantees even if the system crashes at any time, which they achieve using recovery procedures that run after a crash. We present Argosy, a framework for machine-checked proofs of storage systems that supports layered recovery implementations with modular proofs. Reasoning about layered recovery procedures is especially challenging because the system can crash in the middle of a more abstract layer's recovery procedure and must start over with the lowest-level recovery procedure. This paper introduces recovery refinement, a set of conditions that ensure proper implementation of an interface with a recovery procedure. Argosy includes a proof that recovery refinements compose, using Kleene algebra for concise definitions and metatheory. We implemented Crash Hoare Logic, the program logic used by FSCQ [8], to prove recovery refinement, and demonstrated the whole system by verifying an example of layered recovery featuring a write-ahead log running on top of a disk replication system. The metatheory of the framework, the soundness of the program logic, and these examples are all verified in the Coq proof assistant.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3314221.3314585
