This paper introduces the Wave Transactional Filesystem (WTF), a novel, transactional, POSIX-compatible filesystem based on a new file slicing API that enables efficient file transformations. WTF provides transactional access to a distributed filesystem, eliminating the possibility of inconsistencies across multiple files. Further, the file slicing API enables applications to construct files from the contents of other files without having to rewrite or relocate data. Combined, these enable a new class of high-performance applications. Experiments show that WTF can qualitatively outperform the industry-standard HDFS distributed filesystem, up to a factor of four in a sorting benchmark, by reducing I O costs. Microbenchmarks indicate that the new features of WTF impose only a modest overhead on top of the POSIX-compatible API.
Another approach to scalability is demonstrated by Flat Datacenter Storage @cite_31 , which enables applications to access any disk in a cluster via a CLOS network with full bisection bandwidth. To eliminate the scalability bottlenecks inherent to a single master design, FDS stores metadata on its tract servers and uses a centralized master solely to maintain the list of servers in the system. Blizzard @cite_39 builds block storage, visible to applications as a standard block device, on top of FDS, using nested striping and eventual durability to service the smaller writes typical of POSIX applications. These systems are complementary to WTF, and could implement the storage servers abstraction.
@cite_31 @cite_39
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Flat Datacenter Storage (FDS) is a high-performance, fault-tolerant, large-scale, locality-oblivious blob store. Using a novel combination of full bisection bandwidth networks, data and metadata striping, and flow control, FDS multiplexes an application's large-scale I O across the available throughput and latency budget of every disk in a cluster. FDS therefore makes many optimizations around data locality unnecessary. Disks also communicate with each other at their full bandwidth, making recovery from disk failures extremely fast. FDS is designed for datacenter scale, fully distributing metadata operations that might otherwise become a bottleneck. FDS applications achieve single-process read and write performance of more than 2GB s. We measure recovery of 92GB data lost to disk failure in 6.2 s and recovery from a total machine failure with 655GB of data in 33.7 s. Application performance is also high: we describe our FDS-based sort application which set the 2012 world record for disk-to-disk sorting. Existing file systems, even the most scalable systems that store hundreds of petabytes (or more) of data across thousands of machines, store file metadata on a single server or via a shared-disk architecture in order to ensure consistency and validity of the metadata. This paper describes a completely different approach for the design of replicated, scalable file systems, which leverages a high-throughput distributed database system for metadata management. This results in improved scalability of the metadata layer of the file system, as file metadata can be partitioned (and replicated) across a (shared-nothing) cluster of independent servers, and operations on file metadata transformed into distributed transactions. In addition, our file system is able to support standard file system semantics--including fully linearizable random writes by concurrent users to arbitrary byte offsets within the same file--across wide geographic areas. Such high performance, fully consistent, geographically distributed files systems do not exist today. We demonstrate that our approach to file system design can scale to billions of files and handle hundreds of thousands of updates and millions of reads per second-- while maintaining consistently low read latencies. Furthermore, such a deployment can survive entire datacenter outages with only small performance hiccups and no loss of availability. Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, we have designed and built a composable, reconfigurablefabric to accelerate portions of large-scale software services. Each instantiation of the fabric consists of a 6x8 2-D torus of high-end Stratix V FPGAs embedded into a half-rack of 48 machines. One FPGA is placed into each server, accessible through PCIe, and wired directly to other FPGAs with pairs of 10 Gb SAS cables In this paper, we describe a medium-scale deployment of this fabric on a bed of 1,632 servers, and measure its efficacy in accelerating the Bing web search engine. We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system when ranking candidate documents. Under high load, the largescale reconfigurable fabric improves the ranking throughput of each server by a factor of 95 for a fixed latency distribution--- or, while maintaining equivalent throughput, reduces the tail latency by 29 The Panasas file system uses parallel and redundant access to object storage devices (OSDs), per-file RAID, distributed metadata management, consistent client caching, file locking services, and internal cluster management to provide a scalable, fault tolerant, high performance distributed file system. The clustered design of the storage system and the use of client-driven RAID provide scalable performance to many concurrent file system clients through parallel access to file data that is striped across OSD storage nodes. RAID recovery is performed in parallel by the cluster of metadata managers, and declustered data placement yields scalable RAID rebuild rates as the storage system grows larger. This paper presents performance measures of I O, metadata, and recovery operations for storage clusters that range in size from 10 to 120 storage nodes, 1 to 12 metadata nodes, and with file system client counts ranging from 1 to 100 compute nodes. Production installations are as large as 500 storage nodes, 50 metadata managers, and 5000 clients.