In this paper, we propose a new paradigm for network file system design, serverless network file systems. While traditional network file systems rely on a central server machine, a serverless system utilizes workstations cooperating as peers to provide all file system services. Any machine in the system can store, cache, or control any block of data. Our approach uses this location independence, in combination with fast local area networks, to provide better performance and scalability than traditional file systems. Further, because any machine in the system can assume the responsibilities of a failed component, our serverless design also provides high availability via redundant data storage. To demonstrate our approach, we have implemented a prototype serverless network file system called xFS. Preliminary performance measurements suggest that our architecture achieves its goal of scalability. For instance, in a 32-node xFS system with 32 active clients, each client receives nearly as much read or write throughput as it would see if it were the only active client.

No abstract

We demonstrate that high-level le system events exhibit selfsimilar behaviour, but only for short-term time scales of approximately under a day. W e do so through the analysis of four sets of traces that span time scales of milliseconds through months, and that di er in the trace collection method, the lesystems being traced, and the chronological times of the tracing. Two s e t s o f detailed, short-term le system trace data are analyzed; both are shown to have self-similar like behaviour, with consistent Hurst parameters a measure of self-similarity for all le system trafc as well as individual classes of le system events. Long-term le system trace data is then analyzed, and we discover that the traces' high variability and self-similar behaviour does not persist across time scales of days, weeks, and months. Using the short-term trace data, we s h o w that sources of le system trafc exhibit ON OFF source behaviour, which i s c haracterized by highly variably lengthed bursts of activity, followed by similarly variably lengthed periods of inactivity. This ON OFF behaviour is used to motivate a simple technique for synthesizing a stream of events that exhibit the same self-similar short-term behaviour as was observed in the le system traces.

File system designers today face a dilemma. A log-structured file system (LFS) can offer superior performance for many common workloads such as those with frequent small writes, read traffic that is predominantly absorbed by the cache, and sufficient idle time to clean the log. However, an LFS has poor performance for other workloads, such as random updates to a full disk with little idle time to clean. In this paper, we show how adaptive algorithms can be used to enable LFS to provide high performance across a wider range of workloads. First, we show how to improve LFS write performance in three ways: by choosing the segment 'size to match disk and workload characteristics, by modifying the LFS cleaning policy to adapt to changes in disk utilization, and by using cached data to lower cleaning costs. Second, we show how to improve LFS read performance by reorganizing data to match read patterns. Using trace-driven simulations on a combination of synthetic and measured workloads, we demonstrate that these extensions to LFS can significantly improve its performance.