Conquest: Preparing for Life After Disks
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Conquest: Preparing for Life After Disks

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  • Hi, I’m Andy Wang. I’m going to tell you about the conquest file system I built at UCLA.
  • First, I’ll give a brief overview of the talk The problem of modern file system design is that they are designed and optimized for disks. This assumption is problematic in terms of performance and complexity. However, now we have tons of inexpensive RAM. The natural question is “what can we do with that RAM?”
  • The Conquest approach is to combine disk and persistent RAM is an interesting way So, the resulting system is simpler with better performance. In terms of simplification, Conquest code base is 20% smaller than ext2, reiserfs and SGI XFS. In terms of performance, Conquest is 24% to 1900 faster than LRU caching solutions.
  • Here is the outline of the talk I’ll go through motivation, conquest design, major components, performance evaluation, and conclusion.
  • Now here is the full version of the talk. Modern file systems are built for disks. There are two major problems with the disk assumption. First, disk is slow. As a consequence, researchers have been adding all kinds of complexity to mask this performance gap.
  • This graph shows hardware evolution over time. The vertical axis is in log scale. First, CPU and memory are improving at 50% per year. However, disk is improving only at the rate of 15% per year. And, this gap is widening from 5 orders of magnitude to 6 orders of magnitude. Personally, I have trouble to feel those numbers, I try to think of them in the human scale. Back in 1990, if a CPU access took 1 second, it would have taken disk 6 days to perform one access. In 2000, this ratio has grown to 1 second to 3 months.
  • So what’s going on? A disk contains mechanical components—disk arm and disk platters. To access a piece of data, you need to move the disk arm to the correct track, wait for the data to rotate underneath the disk head, and transfer the data.
  • As a result, researchers do all kinds of things to speed up disks. We try to schedule disk arm to minimize the movement. We try to group related information on the surface of the disk. We try to figure out that disk is going to read next and prefetch information. We try to buffer write requests, so we can reorder writes to minimize disk head movement. Also, we use memory to cache information on disk. We try to mirror data on other disks to leverage hardware parallelism.
  • So, what does it take for a single byte of data to traverse from the user space down to the surface of the disk? First, we need to worry about multiple copies of data due to disk cache, or data consistency. To achieve consistency, we need to worry about synchronization. We need to go through a predictive logic of cache replacement, which runs head to head against the predictive readahead logic. In the background, we still have buffered writes, which juggles write requests. After that, we need to find the correct grouping of data, and take the elevator algorithm to land on the disk.
  • So, what are some storage alternatives? This graph plots the speed against the cost for current storage technologies. Both axis are in the log scale. First, we have tape, which is slow and inexpensive. Then, we have disk, which is two orders of magnitude more expensive than tape, but four orders of magnitude faster than tape. Somewhere down the horizon, we start to see flash memory, battery-backed DRAM, magnetic RAM (which is under research), which are commonly referred as the persistent RAM. Persistent RAM is again two orders more expensive than disk, but they are five to six orders faster than disk. If you look at the cost and performance tradeoffs, persistent RAM looks promising.
  • Now, let us look at the price trend over time. Again, the cost is in the log scale. First, let’s see the cost of paper and film, which is a critical barrier to cross for any storage technology to achieve an economy of scale. Once a storage technology crosses this barrier, it becomes cheap enough to become a cheap enough storage alternative. (animate) Now let’s look at various cost curves. For disks, various disk geometries are introduced roughly at the top boundary of the paper and film cost barrier. Also, notice the cost curve for the persistent RAM. Back in 1998, the booming of digital photography changed the slope of the curve. By 2005, we would expect to see 4 to 10 GB of persistent RAM on personal desktops.
  • So, let’s think about these facts for a while. I think that disk will stay around for at least cost reasons. However, now RAM is a viable storage alternative, as we see on PDAs and various devices. However, I expect to see more architectural changes due to RAM, because it is a big change in design assumptions. We need to rethink system components ranging from data structures, interface, to applications.
  • For Conquest, the biggest question is what does it take to design and build a system that assumes lots of persistent RAM as the primary storage medium? The solution is to start from ground up.
  • The idea of Conquest is to design and build a disk/persistent RAM hybrid file system, which delivers all file system services from memory, with the single exception of high-capacity storage. In essence, Conquest provides two separate data paths to memory and disk. Two major benefits are simplicity and performance.
  • By simplicity, Conquest removes disk-related complexities for most files. Also, Conquest makes things simpler for disk as well. Less complexity means fewer bugs, easier maintenance, and shorter data paths.
  • In terms of performance, all management is performed in memory, which will improve overall performance. For memory data path, there is no disk-related overhead. For disk data path, we have faster speed due to simpler usage models, as I will explain later.
  • Conquest consists of the following components. They have to do with how media storage is used, how metadata are represented, how to provide directory service, how to allocate data and metadata, and how to support persistence and resiliency
  • First, let’s revisit the common user access patterns. We know that small files take up relatively little space, and they represent most user accesses. Large files take up most of the space, and they are accessed sequentially most of the time. Of course, database is an exception, and Conquest is not designed for database workload.
  • Based on this user behavior pattern, Conquest stores small files, metadata, executables and shared libraries in RAM. Small files benefit the most from being stored in memory, because disk seek time and rotational delays are dominating overhead for accessing small files. Also, we now have fast byte-level accesses as opposed to block-level accesses. Small files are allocated contiguously. Storing metadata in memory avoids the notorious synchronous update problem. And, it deserves some discussion. Basically, if there is no metadata; there is no file system. Therefore, system designers take extra caution when it comes to handling metadata. If you update a directory, for example, most disk-based file systems will propagate the change synchronously to disk. It is a serious performance problem. By storing in metadata in memory, synchronous updates are a lot faster. Also, we now have a single representation for metadata, as opposed to a runtime representation and storage representation of metadata. Executables and shared libraries are also stored in core, so we can execute programs in-place, which reduces program startup time significantly.
  • Now let’s take a look at the data path for conventional file systems. A storage request has to go through the IO buffer management, which handles caching. If the request is not in the cache, it has to go through persistence support, which is responsible for translating storage and runtime forms of metadata. The request then needs to go through disk management, which handles disk layout, disk arm scheduling and so on before reaching the disk. For conquest memory, updates to metadata and data are in-place. There is no IO buffer management and disk management. Also, for persistence support, we don’t need to translate between runtime and storage states.
  • Since small files and metadata are taken care of, the disk only needs to handle large files. Which means, we can allocate disk space in big chucks, and it translates into lower access and management overhead. Also, without small objects, we don’t need to worry about fragmentation. We don’t need tricks for small files, such as storing data inside the metadata, or elaborate data structures, such as wrapping a balanced tree onto the geometry of the disk cylinders.
  • For large files that are accessed sequentially, disk can deliver near raw bandwidth, which is about 100 MB per second. And that speed is 200 times faster than random disk accesses. Also, large files have well-defined readahead semantics. Since they are read mostly, large file handling involve little synchronization overhead
  • This shows the disk data path of Conquest. Again, on the left side, we have the data path for conventional file systems. Immediately, you see that Conquest data conquest bypasses mechanisms involved in persistence support. The IO buffer management is greatly simplified because we know the behavior of large file accesses. Also, the disk management is greatly simplified due to the lack of small files and fragmentation management. fonts
  • You may ask, “what about large files that are randomly accessed?” In literature, random accesses are commonly defined as nonsequential accesses. However, if you take a look say a movie file, typically it has 150 scene changes. There are 150 places you may randomly jump to, and perform disk accesses sequentially. Also, looking at a mp3 file, the title is stored at the end of the file, so the typical access is to jump to the end of the file and go back to the beginning to play sequentially. Therefore, what may be random accesses are really near sequentially accesses. With this knowledge, we can simplify large-file metadata representation significantly.
  • Before I introduce conquest metadata representation, let’s look at how it’s commonly done in ext2 file systems. First, we have a logical file. If you look into the file, you will find I-node, which contains file attributes and data.
  • However, at the physical level, data is broken into data blocks, because disk is a block oriented device. Therefore, inode has to keep track of data locations as well.
  • Ext2 has the following inode structure to handle both small and large files. For small files, there are ten pointers for fast data accesses. After consuming these ten pointers, we start to use singly indirect block to keep track of the additional data blocks; after that, doubly indirected blocks; and triply indirect blocks. This demonstrates how small files introduce complexity into data structure design. Also, this index block with high fan out is characteristics of disk data structures such as b-tree.
  • So, here are the problems for the ext2 design. First, the metadata is designed for disk storage. The optimization for small files really makes this data structure complex. Also, we have random access data structure for sequential access mostly large files. The data access is dependent on the byte position in a file. In addition, the access time is dependent on the byte position in a file. And, the maximum file size is limited by the number of pointers.
  • Conquest representation is a lot simpler. For persistent RAM, we just hash a file name to the location of data. And just take the offset of the data. As for disk storage, for each file we have a doubly linked list of disk block segments stored in persistent RAM.
  • So, conquest design has direct data access for files that reside in memory. For large files, the worse case is to traverse the doubly linked segment list in memory for random accesses. Also, the maximum file size is now limited by the physical storage.
  • Now, let’s look at how to provide directory service. All of you should be familiar with the ls and dir command, depending on the OS platform. So, directory service should provide fast sequential traversal. Also, it should provide fast random lookup and hardlinks, meaning, providing multiple names to a piece of data.
  • For the first design, I used the double hashing data structure commonly used in compilers. fashion. Double hashing conserves memory size quite well. This data structure can handle sequential traversal by walking through the table. It provides random access by hashing. Hardlinks are handled by hashing two file names to the same data. However, I discovered that when resizing directories, this data structure runs into problems. Conventional directories are maintained as a regular file. Therefore, there is a notion of a current file position. As the hash table rehashes during a resize operation, this file position information is lost. This case is particularly important for the common recursive deletes. Explain more
  • The second design is based on a variant of extensible hash table. Basically, it hashes the same way as any hash table. However, it hashes by using the upper bits of a hash key. When the table resizes, more upper bits are used for hashing, and hash entries can remain in the same relative order. A hardlink is also supported by hashing multiple names to the same piece of data.
  • Extensible hashing shows how an old data structured invented back in 1970’s fits nicely in this RAM-rich environment. However, Conquest still needs to overcome other engineering obstacles. For example, popular hash functions tend to randomize lower bits as opposed to upper bits. Also, maintaining the dynamic file positioning and handling collisions are tricky. There is a constant tension between the memory overhead and complexity tradeoffs. Currently, Conquest is still transitioning to the extensible hash table solution. Move an old data structure to the previous slide
  • Now, let’s move on to metadata allocation. The main functions of metadata allocator is to keep track of unallocated metadata entries. The allocator has to avoid the reuse of metadata IDs since data is uniquely associated with a metadata ID. Also, with a given ID, we should be able to retrieve metadata very quickly.
  • Let’s forget about the metadata allocator for a moment. Let’s take a look at what we have for the existing memory allocation. Memory allocator keeps track of unallocated memory and it makes sure that we don’t have duplicate allocation for the same physical address….
  • Now, lets put two allocators side-by-side. We can see that the existing allocator can already provide the usage status. The physical addresses can be used as unique ids, and they provide fast retrieval of data because once we know the address, we know where the metadata is located. Therefore, we can avoid building the metadata management completely. Basically, whenever we need to allocate metadata, we can use existing memory allocator. The unique id can be obtained by assigning the physical address of metadata being allocated.
  • Persistent support enables a file system to restore state after a reboot. Data and metadata can survive the reboot by not being zeroed out by BIOS. However, memory manager is typically reinitialized after a reboot, which is problematic because it keeps track of metadata allocation. I must provide a way for the memory manager to survive reboots.
  • Let’s first take a look at the structure of Linux memory managers. The Linux memory manager is structured in layers. At the bottom layer, we have the page allocator, which keeps track of individual page allocation and attribute information.
  • Above the page allocator, we have the zone allocator, which divides memory into zones with different purposes. Some examples are IO buffering, DMA, and high memory. Within each zone, memory is allocated in power-of-two sizes.
  • At the top level, we have the slab allocator, which groups allocations by sizes to reduce internal memory fragmentation. For example, if you frequently allocate a data structure of 54 bytes, the slab allocator will allocate a pageful of data structures with that size at a time to reduce allocation overhead and minimize fragmentation.
  • This layered memory management poses some difficulties for conquest to restore the persistent states. First, Conquest need to somehow resurrect three layers of pointer-rich mappings. Second, existing allocators have no notion of persistent and temporary allocations. It is difficult to resurrect memory manager states selectively.
  • Conquest solution is to dynamically create dedicated zones with their instantiations of memory allocators. Since they are instantiations, Conquest can share code with the existing memory manager.
  • In addition, all pointers are encapsulated within each zone means that pointers can survive reboots without serialization and deserialization. Swapping and paging are disabled for Conquest memory zones. However, they are enabled for non-Conquest zones for backward compatibility.
  • For resiliency support. Metadata commit is instantaneous under Conquest, which means there is no need for fsck. Conquest has built-in checkpointing to rollback to the previous file system states. Also, Conquest heavily relies on the pointer-switch commit semantics. Basically, if you want to modify a pointer, you just allocate and initialize a new object, switch the pointer, and deallocate. In the worse case, we will have memory leak that can be garbage collected.
  • Conquest is currently built as a kernel module under linux 2-4-2. It’s fully functional and POSIX compliant. I have modified the memory manager to support Conquest persistence. Currently, the BIOS is the limitation for distribution. UCLA is looking for licensing opportunities at the moment.
  • Now, let’s take a look at the conquest performance. Two aspects of evaluations are architectural simplification and performance improvement. For the architectural simplification, I’ll present the feature count. For performance improvement, I measured memory-only workload, and memory and disk-workload.
  • First, let’s take a look at the conventional data path. It’s color coded. IO buffer management involves buffer allocation, garbage collection, data caching, metadata caching, predictive readahead, write behind, and cache replacement. For persistence support, we need to worry about metadata allocation, metadata placement, and metadata translation. For disk management, we have disk layout and fragmentation management
  • For the memory data path of conquest, the only thing left is the metadata allocation and memory manager encapsulation code. Since metadata allocation is based on existing memory memory, there is really no implementation. However, we do have the additional code to encapsulate memory manager.
  • For the disk data path, conquest avoids metadata caching. Predictive logic is much more lightweight. The persistence support is gone, since metadata is stored in memory, and we have simpler disk management.
  • Now, let’s look at the performance of Conquest. This slide shows the result for PostMark benchmark, which models ISP workload. The graph plots the number of files against the transaction rate. The horizontal axis shows the number of files being accessed, and the vertical access shows the transaction rate. The total file size being exercise is varied from 40 to 250 MB, running with 2GB of physical RAM. The dark blue Conquest is compared against ramfs and other leading disk-based file systems. As you can see, Conquest is performance is comparable to the performance of ramfs. Compared to other disk-based file systems, Conquest is at least 24% faster. Note that all these file systems are operating in the LRU disk cache. File systems optimized for disk does not take full advantage of memory speed.
  • Now let’s fix the number of files to 10,000, and vary the percentage of large files from 0 to 10 percent. Since the working set is larger than memory, the graph does not include the ramfs. As you can see, when both memory and disk components are exercised, Conquest can be still be several times faster than leading disk-based file systems. Here is the boundary of physical RAM. Since we can’t see the right side of the graph too well, let’s zoom into the graph.
  • When the working set is greater than RAM, Conquest still runs 1.4 to 2 times faster than various disk-based file systems. This improvement is very significant.
  • Now let’s look at the microbenchmark numbers. The microbenchmark we chose was the Sprite LFS microbenchmark suite. It is divided into two sets of benchmarks. One is the small file benchmark. It basically create, read, and delete 10,000 files in three separate phases. The dark blue bars represent the operation rate for Conquest. First, you can notice that Conquest creation and deletion are not as fast as ramfs because I didn’t disable the metadata caching inside the VFS. At it turned out; creation and deletion operations account for a relative little fraction of file system accesses. For file read, Conquest is 15% faster than ramfs because Conquest bypasses many disk-related mechanisms used in ramfs.
  • For the modified large file benchmark, there are 5 phases—sequential write, sequential read, random write, random read, and sequential read. Each phase iterates through 10 files of 1MB each. The vertical axis shows the bandwidth for various file systems. Conquest outperforms ramfs by 8-15% for various operations. Of course, you may ask, what would happen if files are bigger than 1MB.
  • This graph shows the Conquest performance with 1.01 MB files. For reads, Conquest falls back to the speed of IO buffer. For writes, Conquest also commits changes to disk.
  • For 100 MB files, Conquest performance matches leading disk-based file systems. Therefore, Conquest has done no harm for the disk performance. Also, Conquest currently is using 4KB access granularity as opposed to 256 KB access granularity, there is still room for improving the disk bandwidth.
  • So far, you see that Conquest is doing great. However, I have gone a long way to reach this kind of performance numbers. I can still recall that one year ago, I was discussing some puzzling microbenchmark numbers with my colleague, Geoff Kuenning. He told me that “if Conquest is slower than ext2, I will toss you off the balcony.”
  • So, with me hanging off a balcony, I discovered some odd numbers with the original large-file microbenchmark. Basically, the benchmark runs those file operations on a single 1 MB file--Sequential write on a file with fsync, sequential read of the same file. Random write on a new file, random read of the same file, and sequential read of the same file.
  • So, I first try to evade Geoff by pointing out that “isn’t it strange that random reads are slower than sequential reads in memory?” Geoff told me, “well, it’s easy to explain. Random reads are not aligned in the benchmark.”
  • Then, he asked “why are RAM-based file systems run slower than disk-based file systems?”
  • So, I have gone through a series of hypotheses. Could it be some kind of warm-up effect? Maybe. However, why do RAM-based systems warm up slower than disk-based systems? Did we have bad initial states? I examined the initial benchmark condition and found nothing. How about the Pentium III streaming IO option? Is it possible that ramfs triggers the streaming IO and leaves L2 with little content to reuse for the subsequent read operation. From the profiling, I found nothing.
  • Finally, I tracked down the effects of cache footprint on the microbenchmark performance. On the left, I have a large cache footprint for a file system, and on the right, I have a small cache footprint for a file system. For the large cache footprint, after writing a file sequentially, the cache is left some of the dirty cache lines from the end of the file. For the subsequent sequential read, the cache is likely to evict the dirty content before reading in the file again from the beginning. However, if we have a small active cache footprint, we will have more room to cache the dirty writes. For the subsequent read, cache has to evict more dirty content before reading in the beginning of the file. The lesson here is that a smaller cache footprint can leave more room to cache dirty writes, which can amplify the performance swing for the subsequent operation. Of course, this result also depends on the relative sizes of the file and L2 cache.
  • After considering the memory and L2 caching effects into the microbenchmark, we obtain the following graph. It’s interesting to see that random accesses in memory can be faster than sequential accesses due to the reuse of cache content.
  • After considering the memory and L2 caching effects into the microbenchmark, we obtain the following graph. It’s interesting to see that random accesses in memory can be faster than sequential accesses due to the reuse of cache content.
  • I have walked into this project not expecting Conquest to perform better than LRU. However, not only Conquest outperforms LRU, it also outperforms ramfs in many circumstances. This lesson shows that the disk handling is very heavyweight and imposes severe penalty for accessing memory content. Also, matching user access patterns and storage media offers considerable simplification and performance improvement. This result is not automatic. Since Conquest has two separate data path, it is quite possible that the combined memory footprint is larger than before, and result in poorer performance. The system needs careful design.
  • For the performance measurement, the effects of L2 caching are becoming very visible for memory workloads. This lesson is particularly important because modern workloads are more and more memory intensive. Also, we cannot just blindly apply existing disk-based microbenchmarks to measure the memory performance of file systems. We really need to consider states of L2 cache and memory behavior at each stage of microbenchmarking.
  • One additional lesson is that don’t discuss your performance numbers next to a balcony…unless….you are romeo and juliet…
  • Now, let’s look at some related work. The first one is disk caching. The fundamental difference between disk caching and Conquest is that disk caching assume the scarcity of memory resources. Therefore, disk caching cares a lot about moving information back and forward being memory and disk in a speculative manner. Disk caching introduces complex mechanisms to maintain consistency between memory and disk, especially with the presence of metadata. For RAM drives and RAM file systems, they are not meant to be persistent, and they use disk-related mechanisms to access memory content. Also, they are limited by the storage capacity.
  • Disk emulators, or commonly known as the solid-state disk, are memory blocks connected through the SCSI interface. As one paper indicates, the use of SCSI interface can impose 45% to 55% performance penalty on the performance of disk emulators. There are also various ad hoc approaches, such as manual transferring of files to and from ramfs. This approach has capacity limitation, and you also need to know which part of the file system should be used in memory as opposed to disk. Also, this process can be automated through a background daemon. But, there are semantic and name space problems such as the semantics of mounting, links, and handling the location of storage.
  • So, what’s after Conquest? We have learned that the principle of matching user access patterns and storage media result in better system performance. The natural extension is to apply this principle to the distributed domain, especially with heterogeneous machines. The reason why I mention heterogeneity is that I see heterogeneous machines can introduce more opportunities for specialization. It would be interesting to explore specialization within a cluster. Preferably, this can be achieved in a self-organizing and self-evolving manner. More and more modern systems are designed with the philosophy of statelessness. However, it does not mean that stateful computing is fading away. In fact, the extensive use of caching to improve performance shows the importance of stateful computing, and Conquest further advocates the direction of the state-rich computing. Basically, in additional to caching data, we can cache runtime data structures to improve system performance. This concept is similar to /tmp. Instead of storing files, we store data structures.
  • The concept of separating the storage of metadata and data opens up more possibilities, because we have a greater flexibility to associate metadata and data. For example, metadata can be associated with data with different fidelity on computing devices of different caliber. It is interesting to know how we can use this characteristic to replicate data across a PDA, which has only memory storage, laptop, which has limited disk storage, and desktop, which has a large storage capacity. Although the computing world is moving toward the memory-rich environment, but file system benchmarks are still designed to measure the disk performance. Therefore, it is important to design new memory benchmarks that take considerations of underlying behaviors of hardware.
  • In this research, I have contributed early insights to the design and implementation of disk-memory hybrid file systems. I have demonstrates that a system can achieve performance and simplicity at the same time. Also, I have identified that disk-based microbenchmarks are not suitable to measure the memory performance of file systems. And, Conquest has opened doors to many exciting areas of research.
  • Conquest demonstrates how rethinking changes in underlying assumptions can lead to significant architectural and performance improvements. This thinking process can be applied to other areas of operating systems as well.
  • Memory reliable? I didn’t change the protection mechanism; also, Google Paging and swapping? Back to the high overhead situation Workload representative? Show disk caching is not as fast as the memory performance Do we have enough RAM? 1G is enough to run Conquest I didn’t disable metadata caching…requires changes in an internal access interface.

Conquest: Preparing for Life After Disks Conquest: Preparing for Life After Disks Presentation Transcript

  • Conquest : Preparing for Life After Disks CS239 Seminar October 24, 2002 An-I Andy Wang University of California, Los Angeles
  • Conquest Overview
    • File systems are optimized for disks
      • Performance problem
      • Complexity
    • Now we have tons of inexpensive RAM
    • What can we do with that RAM?
  • Conquest Approach
    • Combine disk and persistent RAM (e.g., battery-backed RAM) in a novel way
      • Simplification
        • > 20% fewer semicolons than ext2, reiserfs, and SGI XFS
      • Performance (under popular benchmarks)
        • 24% to 1900% faster than LRU disk caching
  • Outline of the Talk
    • Motivation
    • Conquest design (high level)
    • Conquest components
    • Performance evaluation
    • Conclusion
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Motivation
    • Most file systems are built for disks
    • Problems with the disk assumption:
      • Performance
      • Complexity
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Hardware Evolution 1990 2000 1 KHz 1 MHz 1 GHz CPU (50% /yr) memory (50% /yr) disk (15% /yr) accesses per second (log scale) 1995 (1 sec : 6 days) (1 sec : 3 months) Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion 10 5 10 6
  • Inside Pandora’s Box
    • Disk arm
    • Disk platters
    • Access time = seek time (disk arm)
    • + rotational delay (disk platter)
    • + transfer time
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Disk Optimization Methods
    • Disk arm scheduling
    • Group information on disk
    • Disk readahead
    • Buffered writes
    • Disk caching
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
    • Data mirroring
    • Hardware parallelism
  • Complexity Bytes Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion synchronization predictive readahead cache replacement elevator algorithm data clustering data consistency asynchronous write
  • Storage Media Alternatives accesses/sec (log scale) $/MB (log scale) 10 0 10 3 10 -3 10 -3 10 6 Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion persistent RAM magnetic RAM? (write once) flash memory disk tape battery-backed DRAM
  • Price Trend of Persistent RAM 1995 2005 10 0 year $/MB (log scale) 2000 10 -2 10 -1 10 1 10 2 Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion paper/film 3.5 " HDD 2.5 " HDD 1 " HDD persistent RAM booming of digital photography 4 to 10 GB of persistent RAM
  • Old Order; New World
    • Disk will stay around
      • Cost, capacity, power, heat
    • RAM as a viable storage alternative
      • PDAs, digital cameras, MP3 players
    • More architectural changes due to RAM
      • A big assumption change from disk
      • Rethink data structures, interfaces, applications
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
    • What does it take to design and build a system that assumes ample persistent RAM as the primary storage medium?
    Getting a Fresh Start Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Conquest Design
    • Design and build a disk/persistent-RAM hybrid file system
    • Deliver all file system services from memory, with the exception of high-capacity storage
    • Two separate data paths to memory and disk
    • Benefits:
      • Simplicity
      • Performance
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Simplicity
    • Remove disk-related complexities for most files
    • Make things simpler for disk as well
    • Less complexity
      • Fewer bugs
      • Easier maintenance
      • Shorter data paths
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
    • Overall
      • All management performed in memory
    • Memory data path
      • No disk-related overhead
    • Disk data path
      • Faster speed due to simpler access models
    Performance Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Conquest Components
    • Media management
    • Metadata representation
    • Directory service
    • Allocation service
    • Persistence support
    • Resiliency support
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • User Access Patterns
    • Small files
      • Take little space (10%)
      • Represent most accesses (90%)
    • Large files
      • Take most space
      • Mostly sequential accesses
    • Not characteristic of database applications
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Files Stored in Persistent RAM
    • Small files (< 1MB)
      • No seek time or rotational delays
      • Fast byte-level accesses
      • Contiguous allocation
    • Metadata
      • Fast synchronous update
      • No dual representations
    • Executables and shared libraries
      • In-place execution
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Memory Data Path of Conquest Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion Conventional File Systems IO buffer disk management storage requests IO buffer management disk persistence support Conquest Memory Data Path storage requests persistence support battery-backed RAM small file and metadata storage
  • Large-File-Only Disk Storage
    • Allocate in big chunks
      • Lower access overhead
      • Reduced management overhead
    • No fragmentation management
    • No tricks for small files
      • Storing data in metadata
    • No elaborate data structures
      • Wrapping a balanced tree onto disk cylinders
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Sequential-Access Large Files
    • Sequential disk accesses
      • Near-raw bandwidth
    • Well-defined readahead semantics
    • Read-mostly
      • Little synchronization overhead (between memory and disk)
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Disk Data Path of Conquest Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion Conventional File Systems IO buffer disk management storage requests IO buffer management disk persistence support Conquest Disk Data Path IO buffer management IO buffer storage requests disk management disk battery-backed RAM small file and metadata storage large-file-only file system
  • Random -Access Large Files
    • Random access?
      • Common definition: nonsequential access
      • A typical movie has 150 scene changes
      • MP3 stores the title at the end of the files
    • Near sequential access?
      • Simplifies large-file metadata representation significantly
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Logical File Representation File Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion Name(s)
    • i -node
      • File attributes
    • Data
  • Physical File Representation File Name(s) Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
    • i -node
      • File attributes
      • Data locations
    • Data blocks
  • Ext2 Data Representation i -node Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion data block location index block location index block location index block location data block location index block location index block location data block location data block location 10 data block location data block location data block location data block location index block location
  • Disadvantages with Ext2 Design
    • Designed for disk storage
    • Optimization for small files makes things complex
    • Random-access data structure for large files that are accessed mostly sequentially
    • Data access time dependent on the byte position in a file
    • Maximum file size is limited
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Conquest Representation
    • Persistent RAM
      • Hash(file name) = location of data
      • Offset(location of data)
    • Disk storage
      • Per-file, doubly linked list of disk block segments (stored in persistent RAM)
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Advantages Conquest Design
    • Direct data access for in-core files
    • Worse case: sequential memory search for random disk locations
    • Maximum file size limited by physical storage
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Directory Service
    • Requirements
      • Fast sequential traversal (e.g., ls)
      • Fast random lookup (e.g., locate file x)
      • Hard links (apply multiple names to data)
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • First Design
    • A doubly hashed table for each directory
      • Conserves space
    • Problems:
      • Dynamic resizing of directories
      • Need to handle the current file position
      • Important for rm -fr
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Second Design
    • A variant of extensible hash table for each directory
    • An old data structure fits nicely
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion empty empty 0 100 | file_1 1 001 | file_2 empty empty 01 00 | file1 10 01 | file2 empty 00 11 | dir1 11 10 | file2_hardlink
  • Additional Engineering Details
    • Popular hash functions randomize lower bits
    • Dynamic file positioning
    • Need to handle collisions
    • Memory overhead and complexity tradeoffs
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Metadata Allocation
    • Requirements
      • Keep track of usage status of metadata entries
      • Avoid duplicate allocation with unique IDs
      • Fast retrieval of metadata with a given ID
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion ID: 1| free ID: 2| in use ID: 3| free ID: 4| free ID: 5| in use ID: 6| free
  • Existing Memory Allocation
    • Services
      • Keep track of unallocated memory
      • No duplicate allocation of physical addresses
    • Hmm…
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion ADDR 0xe000000| free ADDR 0xe000038| in use ADDR 0xe000070| free ADDR 0xe0000A8| free ADDR 0xe0000E0| free ADDR 0xe000118| in use
  • Conquest Metadata Management
    • Metadata = memory allocated by memory manager
    • Metadata ID = physical address of metadata
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion ADDR 0xe000000| free ADDR 0xe000038| in use ADDR 0xe000070| free ADDR 0xe0000A8| free ADDR 0xe0000E0| free ADDR 0xe000118| in use ID: 1| free ID: 2| in use ID: 3| free ID: 4| free ID: 5| in use ID: 6| free Usage status Unique IDs and fast retrieval
  • Persistence Support
    • Restore file system states after a reboot
      • Data
      • Metadata
      • Memory manager
        • Keep track of metadata allocation
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Linux Memory Manager (1)
    • Page allocator maintains individual pages
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion Page allocator
  • Linux Memory Manager (2)
    • Zone allocator allocates memory in power-of-two sizes
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion Page allocator Zone allocator
  • Linux Memory Manager (3)
    • Slab allocator groups allocations by sizes to reduce internal memory fragmentation
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion Page allocator Zone allocator Slab allocator
  • Linux Memory Manager (4)
    • Difficult to restore the persistent states
      • Three layers of pointer-rich mappings
      • Mixing of persistent and temporary allocations
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion Page allocator Slab allocator Zone allocator
  • Conquest Persistence
    • Create memory zones with own instantiations of memory managers
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion Page allocator Slab allocator Zone allocator
  • Conquest Persistence
    • Encapsulate all pointers within each zone
    • Pointers can survive reboots
    • No serialization and deserialization
    • Swapping and paging
      • Disabled for Conquest memory zones
      • Enabled for non- Conquest zones
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Resiliency Support
    • Instantaneous metadata commit
      • No fsck (ad hoc metadata consistency check)
    • Built-in checkpointing
    • Pointer-switch commit semantics
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion pointer pointer
  • Implementation Status
    • Kernel module under Linux 2.4.2
    • Fully functional and POSIX compliant
    • Modified memory manager to support Conquest persistence
    • Need to overcome BIOS limitations for distribution
    • Looking for licensing opportunities
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Performance Evaluation
    • Architectural simplification
      • Feature count
    • Performance improvement
      • Memory-only workload
      • Memory and disk workload
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Conventional Data Path
    • Buffer allocation management
    • Buffer garbage collection
    • Data caching
    • Metadata caching
    • Predictive readahead
    • Write behind
    • Cache replacement
    • Metadata allocation
    • Metadata placement
    • Metadata translation
    • Disk layout
    • Fragmentation management
    Conventional File Systems IO buffer disk management storage requests IO buffer management disk persistence support Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Memory Path of Conquest
    • Buffer allocation management
    • Buffer garbage collection
    • Data caching
    • Metadata caching
    • Predictive readahead
    • Write behind
    • Cache replacement
    • Metadata allocation
    • Metadata placement
    • Metadata translation
    • Disk layout
    • Fragmentation management
    Conquest Memory Data Path storage requests Persistence support battery-backed RAM small file and metadata storage Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
    • Memory manager encapsulation
  • Disk Path of Conquest
    • Buffer allocation management
    • Buffer garbage collection
    • Data caching
    • Metadata caching
    • Predictive readahead
    • Write behind
    • Cache replacement
    • Metadata allocation
    • Metadata placement
    • Metadata translation
    • Disk layout
    • Fragmentation management
    Conquest Disk Data Path IO buffer management IO buffer storage requests disk management disk battery-backed RAM small file and metadata storage large-file-only file system Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
    • Conquest is comparable to ramfs
    • At least 24% faster than the LRU disk cache
    PostMark Benchmark (1)
    • ISP workload (emails, web-based transactions)
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion 40 to 250 MB working set with 2 GB physical RAM
    • When both memory and disk components are exercised, Conquest can be several times faster than ext2fs , reiserfs , and SGI XFS
    PostMark Benchmark (2) Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion 10,000 files, 80 MB to 3.5 GB working set with 2 GB physical RAM > RAM <= RAM
    • When working set > RAM, Conquest is 1.4 to 2 times faster than ext2fs , reiserfs , and SGI XFS
    PostMark Benchmark (3) Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion 10,000 files, 80 MB to 3.5 GB working set with 2 GB physical RAM
  • Sprite LFS Microbenchmarks (1)
    • Small-file benchmark
      • Operates on 10,000 1-KB files in three phases
    Motivation – Conquest Alternatives – Conquest Design – Performance Evaluation – Conclusion
  • Sprite LFS Microbenchmarks (2)
    • Modified large-file microbenchmark: 10 1-MB files ( Conquest in-core files)
    Motivation – Conquest Alternatives – Conquest Design – Performance Evaluation – Conclusion
  • Sprite LFS Microbenchmarks (3)
    • Modified large-file microbenchmark: 10 1.01-MB files ( Conquest on-disk files)
    Motivation – Conquest Alternatives – Conquest Design – Performance Evaluation – Conclusion
  • Sprite LFS Microbenchmarks (4)
    • Large-file microbenchmark: 40 100-MB files ( Conquest on-disk files)
    Motivation – Conquest Alternatives – Conquest Design – Performance Evaluation – Conclusion
  • History’s Mystery
    • Puzzling Microbenchmark Numbers…
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion Geoffrey Kuenning: “ If Conquest is slower than ext2 , I will toss you off of the balcony…”
  • With me hanging off a balcony…
    • Original large-file microbenchmark: 1-MB file ( Conquest in-core file)
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Odd Microbenchmark Numbers
    • Why are random reads slower than sequential reads?
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Odd Microbenchmark Numbers
    • Why are RAM-based file systems slower than disk-based file systems?
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • A Series of Hypotheses
    • Warm-up effect?
      • Maybe
      • Why do RAM-based systems warm up slower?
    • Bad initial states?
      • No
    • Pentium III streaming IO option?
      • No
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Effects of Cache Footprint Sizes
    • Large cache footprint
    • Small cache footprint
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion footprint footprint write a file sequentially footprint file end read the same file sequentially footprint flush file end file read write a file sequentially footprint file end read the same file sequentially footprint flush file end read file
  • LFS Sprite Microbenchmarks
    • Modified large-file microbenchmark: 10 1-MB files ( Conquest in-core files)
    Motivation – Conquest Alternatives – Conquest Design – Performance Evaluation – Conclusion faster random over sequential accesses due to cache reuse
  • LFS Sprite Microbenchmarks (2)
    • Modified large-file microbenchmark: 10 128-KB files ( Conquest in-core files)
    Motivation – Conquest Alternatives – Conquest Design – Performance Evaluation – Conclusion slower random over sequential accesses due to the extra lseek
  • Lessons Learned
    • Faster than LRU caching, unexpected
      • Heavyweight disk handling
      • Severe penalty for accessing memory content
    • Matching user access patterns to storage media offers considerable simplification and better performance
      • Not an automatic result
      • Need careful design
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • More Lessons Learned
    • Effects of L2 caching become highly visible in memory workloads (modern workloads)
    • Cannot blindly apply existing disk-based microbenchmarks to measure memory performance of file systems
    • Need to consider states of L2 cache and memory behaviors at each stage of microbenchmarking
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Additional Lessons Learned
    • Don’t discuss your performance numbers next to a balcony…unless…
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Related Work (1)
    • Disk caching
      • Assumption of scarce memory
      • Complex mechanisms to maintain consistency
        • Especially with the presence of metadata
    • RAM drives and RAM file systems
      • Not meant to be persistent
      • Use disk-related mechanisms
      • Limitations on storage capacity
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Related Work (2)
    • Disk emulators
      • RAM storage accessed through SCSI interface
    • Ad hoc approaches
      • Manual transferring of files to and from ramfs
        • Capacity limitation
      • Background daemon to stage RAM files to a disk
        • Semantic and name space problems
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Going Beyond Conquest (1)
    • Matching usage patterns with heterogeneous machines in the distributed domain
      • Specialized tasks for machines within a cluster
      • Preferably self-organizing and self-evolving
    • State-rich computing
      • Caching of runtime data structures
      • Similar to /tmp
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Going Beyond Conquest (2)
    • Separate storage of metadata from data
      • Association of metadata with data of different fidelity
      • Opportunity for hierarchical replication across devices with different calibers
    • Benchmarking memory performance of file systems
      • Developing new memory benchmarks
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Contributions
    • Demonstrated the feasibility of disk-memory hybrid file systems
    • Showed performance does not preclude simplicity
    • Pinpointed cache-related problems with modern benchmarks
    • Opened doors to many exciting areas of research
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Conclusion
    • Conquest demonstrates how rethinking changes in underlying assumptions can lead to significant architectural and performance improvements
    • Radical changes in hardware, applications, and user expectations in the past decade should lead us to rethink other aspects of OS as well.
    Motivation – Conquest Design – Conquest Components – Performance Evaluation – Conclusion
  • Questions . . . Conquest: http://lasr.cs.ucla.edu/conquest Andy Wang: [email_address]