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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 𝜆FS, an elastic, high- performance metadata service for large-scale DFSes. 𝜆FS 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. 𝜆FS takes full advantage of the unique benefits offered by FaaS—elastic scaling and massive parallelism—to realize a highly-optimized metadata service capable of sustaining up to 4.13× higher throughput, 90.40% lower latency, 85.99% lower cost, 3.33× better performance-per-cost, and better resource utilization and efficiency than a state-of-the-art DFS for an industrial workload 

File systems have many configuration parameters. Such flexibility comes at the price of additional complexity which could lead to subtle configuration-related issues. To address the challenge, we study the potential configuration dependencies of a representative file system (i.e., Ext4), and identify a prevalent pattern called multi-level configuration dependencies. We build a static analyzer to extract the dependencies and leverage the information to address different configuration issues. Our preliminary prototype is able to extract 64 multi-level dependencies with a low false positive rate. Additionally, we can identify multiple configuration issues effectively.