{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/l-fs-a-scalable-and-elastic-distributed-file","title":"$λ$FS: A Scalable and Elastic Distributed File System Metadata Service using Serverless Functions","arxiv_id":"2306.11877","date":"2023-06-20","proceeding":null,"authors":["Benjamin Carver","Runzhou Han","Jingyaun Zhang","Mai Zheng","Yue Cheng"],"abstract":"The metadata service (MDS) sits on the critical path for distributed file system (DFS) operations, and therefore it is key to the overall performance of a large-scale DFS. Common \"serverful\" MDS architectures, such as a single server or cluster of servers, have a significant shortcoming: either they are not scalable, or they make it difficult to achieve an optimal balance of performance, resource utilization, and cost. A modern MDS requires a novel architecture that addresses this shortcoming. To this end, we design and implement $\\lambda$FS, an elastic, high-performance metadata service for large-scale DFSes. $\\lambda$FS scales a DFS metadata cache elastically on a FaaS (Function-as-a-Service) platform and synthesizes a series of techniques to overcome the obstacles that are encountered when building large, stateful, and performance-sensitive applications on FaaS platforms. $\\lambda$FS takes full advantage of the unique benefits offered by FaaS $\\unicode{x2013}$ elastic scaling and massive parallelism $\\unicode{x2013}$ to realize a highly-optimized metadata service capable of sustaining up to 4.13$\\times$ higher throughput, 90.40% lower latency, 85.99% lower cost, 3.33$\\times$ better performance-per-cost, and better resource utilization and efficiency than a state-of-the-art DFS for an industrial workload.","url_abs":"https://arxiv.org/abs/2306.11877v1","url_pdf":"https://arxiv.org/pdf/2306.11877v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"l-fs-a-scalable-and-elastic-distributed-file","repo_url":"https://github.com/ds2-lab/lambdafs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"l-fs-a-scalable-and-elastic-distributed-file","repo_url":"https://github.com/ds2-lab/lambdafs-benchmark-utility","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}