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The typical latency for accessing data on disk is in the range of tens of milliseconds. When compared to the 2 nanosecond clock step of a typical 500 megahertz processor, this is very slow (5,000,000 times slower). The result is that I/O cache misses will force fast CPUs to sit idle while waiting for I/O to complete. This difference between processor and disk speeds is referred to as the I/O gap [22]. Prefetching methods based on sequential heuristics are only able to partially address the I/O gap, leaving a need for more intelligent methods of prefetching file data.

Caching recently accessed data is helpful, but without prefetching its benefits can be limited. By loading data in anticipation of upcoming needs, prefetching can turn a 20 millisecond disk access into a 100 microsecond page cache hit. This is why most I/O systems prefetch extensively based on a sequential heuristic. For example, disk controllers frequently do read-ahead (prefetching of the next disk block), and file systems often prefetch the next sequential page within a file. In both of these cases, prefetching is a heuristic guess that accesses will be sequential and can be done because there is sequential structure to the data abstraction. However, once a file system reaches the end of a file it typically has no notion of ``next file'' and is unable to continue prefetching.

Despite this lack of sequential structure there are still strong relationships that exist between files and cause file accesses to be correlated. Several studies [12,13,6,23,16,3,18,25] have shown that predictable reference patterns are quite common, and offer enough information for significant performance improvements. Previously, we used traces of file system activity to demonstrate the extent of the relationships between files [13]. These traces covered all system calls over a one month period on four separate machines. From these traces we observed that a simple last successor prediction model (which predicts that an access to file A will be followed by the same file that followed the last access to A) correctly predicted 72% of file access events. We also presented a more accurate technique called Partitioned Context Modeling (PCM) that efficiently handles the large number of distinct files and adapts to changing reference patterns.

To demonstrate the effectiveness of using file access relationships to improve I/O performance we added predictive prefetching to the Linux kernel. We enhanced the normal Linux file system cache by adding two components, a model that tracks the file reference patterns and a prefetch engine that uses the model's predictions to select and prefetch files that are likely to be requested. Figure 1 illustrates how these components integrate into an I/O cache (the shaded area indicates the new components).

Figure: Cache system with predictive prefetching.

To evaluate our implementations we used four application-based benchmarks, the compile phase of the Andrew benchmark [7], the linking of the Linux kernel, a Glimpse text search indexing [20] of the /usr/doc directory, and a compiling, patching and re-compiling of the SSH source code versions 1.2.18 through 1.2.31. For both last successor and Partitioned Context Model (PCM) based prefetching, we observed that predicting only the next event limited the effectiveness of predictive prefetching. To address this limitation, we modified PCM to create a variation called Extended Partitioned Context Modeling (EPCM), which predicts sequences of upcoming accesses, instead of just the next access. Our tests showed that EPCM based predictive prefetching reduced I/O latencies, from 31% to as much as 90%, and that total elapsed time was reduced by 11% to 16%. We concluded that a predictive prefetching system has the potential to significantly reduce I/O latency and is effective in improving overall performance.

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Next: Modeling Background Up: Design and Implementation of Previous: Abstract
Tom M. Kroeger