We describe how to manage distributed file system caches based upon groups of files that are accessed together We use file access patterns to automatically construct dynamic groupings of files and then manage our cache by fetching groups, rather than single files. We present experimental results, based on trace-driven work-loads, demonstrating that grouping improves cache performance. At the file system client, grouping can reduce LRU demand fetches by 50 to 60%. At the server, cache hit rate improvements are much more pronounced, but vary widely (20 to over 1200%) depending upon the capacity of intervening caches. Our treatment includes information theoretic results that justifY our approach to file grouping.

Distributed file systems are often used to replicate a Web site's content among its many servers. However, for content that needs to be dynamically updated and distributed to many servers, file system locking protocols exhibit high latency and heavy network usage. Poor performance arises because the Web-serving workload differs from the assumed workload. To address the shortcomings of file systems, we introduce the publish consistency model well suited to the Web-serving workload and implement it in the producer-consumer locking protocol. A comparison of this protocol against other file system protocols by simulation shows that producer-consumer locking removes almost all latency due to protocol overhead and significantly reduces network load.

We describe a novel on-line file access predictor, Recent Popularity, capable of rapid adaptation to workload changes while simultaneously predicting more events with greater accuracy than prior efforts. We distinguish the goal of predicting the most events accurately from the goal of offering the most accurate predictions (when declining to offer a prediction is acceptable). For this purpose we present two distinct measures of accuracy, general and specific accuracy, corresponding to these goals. We describe how our new predictor and an earlier effort, Noah, can trade the number of events predicted for prediction accuracy by modifying simple parameters. When prediction accuracy is strictly more important than the number of predictions offered, trace-based evaluation demonstrates error rates as low as 2%, while offering predictions for more than 60% of all file access events.

File systems provide an interface for applications to obtain exclusive access to files, in which a process holds privileges to a file that cannot be preempted and restrict the capabilities of other processes. Local file systems do this by maintaining information about the privileges of current file sessions, and checking subsequent sessions for compatibility. Implementing exclusive access in this manner for distributed file systems degrades performance by requiring every new file session to be registered with a lock server that maintains global session state. We present two techniques for improving the performance of session management in the distributed environment. We introduce a distributed lock for managing file access, called a semi-preemptible lock, that allows clients to cache privileges. Under a semi-preemptible lock, a file system creates new sessions without messages to the lock manager. This improves performance by exploiting locality – the affinity of files to clients. We also present data structures and algorithms for the dynamic evaluation of locks that allow a distributed file system to efficiently manage arbitrarily complex locking. In this case, complex means that an object can be locked in a large number of unique modes. The combination of these techniques results in a distributed locking scheme that supports fine-grained concurrency control with low memory and message overhead and with the assurance that their locking system is correct and avoids unnecessary deadlocks.