Nitin Gupta wrote:
Frequently accessed filesystem data is stored in memory to reduce access to (much) slower backing disks. Under memory pressure, these pages are freed and when needed again, they have to be read from disks again. When combined working set of all running application exceeds amount of physical RAM, we get extereme slowdown as reading a page from disk can take time in order of milliseconds. Memory compression increases effective memory size and allows more pages to stay in RAM. Since de/compressing memory pages is several orders of magnitude faster than disk I/O, this can provide signifant performance gains for many workloads. Also, with multi-cores becoming common, benefits of reduced disk I/O should easily outweigh the problem of increased CPU usage. It is implemented as a "backend" for cleancache_ops [1] which provides callbacks for events such as when a page is to be removed from the page cache and when it is required again. We use them to implement a 'second chance' cache for these evicted page cache pages by compressing and storing them in memory itself. We only keep pages that compress to PAGE_SIZE/2 or less. Compressed chunks are stored using xvmalloc memory allocator which is already being used by zram driver for the same purpose. Zero-filled pages are checked and no memory is allocated for them. A separate "pool" is created for each mount instance for a cleancache-aware filesystem. Each incoming page is identified with <pool_id, inode_no, index> where inode_no identifies file within the filesystem corresponding to pool_id and index is offset of the page within this inode. Within a pool, inodes are maintained in an rb-tree and each of its nodes points to a separate radix-tree which maintains list of pages within that inode. While compression reduces disk I/O, it also reduces the space available for normal (uncompressed) page cache. This can result in more frequent page cache reclaim and thus higher CPU overhead. Thus, it's important to maintain good hit rate for compressed cache or increased CPU overhead can nullify any other benefits. This requires adaptive (compressed) cache resizing and page replacement policies that can maintain optimal cache size and quickly reclaim unused compressed chunks. This work is yet to be done. However, in the current state, it allows manually resizing cache size using (per-pool) sysfs node 'memlimit' which in turn frees any excess pages *sigh* randomly. Finally, it uses percpu stats and compression buffers to allow better performance on multi-cores. Still, there are known bottlenecks like a single xvmalloc mempool per zcache pool and few others. I will work on this when I start with profiling. * Performance numbers: - Tested using iozone filesystem benchmark - 4 CPUs, 1G RAM - Read performance gain: ~2.5X - Random read performance gain: ~3X - In general, performance gains for every kind of I/O Test details with graphs can be found here: http://code.google.com/p/compcache/wiki/zcacheIOzone If I can get some help with testing, it would be intersting to find its effect in more real-life workloads. In particular, I'm intersted in finding out its effect in KVM virtualization case where it can potentially allow running more number of VMs per-host for a given amount of RAM. With zcache enabled, VMs can be assigned much smaller amount of memory since host can now hold bulk of page-cache pages, allowing VMs to maintain similar level of performance while a greater number of them can be hosted.
So why would someone want to use zram if they have transparent page cache compression with zcache? That is, why is this not a replacement for zram?
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