COMP520-12C Final Report. NomadFS A block migrating distributed file system

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1 COMP520-12C Final Report NomadFS A block migrating distributed file system Samuel Weston This report is in partial fulfilment of the requirements for the degree of Bachelor of Computing and Mathematical Sciences with Honours (BCMS(Hons)) at The University of Waikato Samuel Weston

2 Abstract A distributed file system is a file system that is spread across multiple machines. This report describes the block-based distributed file system NomadFS. NomadFS is designed for small scale distributed settings, such as those that exist in computer laboratories and cluster computers. It implements features, such as caching and block migration, which are aimed at improving the performance of shared data in such a setting. This report includes a discussion of the design and implementation of NomadFS, including relevant background. It also includes performance measurements, such as scalability. 2

3 Acknowledgements I would like to thank all the friendly members of the WAND network research group. This especially includes my supervisor Tony McGregor who has provided me with a massive amount of help over the year. Thanks! On a personal level I have enjoyed developing NomadFS and have learnt a great deal as a consequence of this development. This learning includes improving my C programming ability, both in user space and kernel space (initially NomadFS was planned to be developed as a kernel space file system). I have also learnt a large amount about file systems and operating systems in general. 3

4 nomad / n@umæd/ noun member of tribe roaming from place to place for pasture; 4

5 Contents 1 Introduction 11 2 Background A file system overview System calls Distributed systems Communication Synchronisation and Consistency Fault Tolerance Performance Scalability Transparency The Linux Virtual File System Filesystem in Userspace Summary Goals 22 4 File System Survey Network File System (NFS) Gluster File System (GlusterFS) Google File System Zebra and RAID Summary Design Overview Block interface Block-based approach Identification and locality

6 5.3 File system structure Communication API Performance and Reliability Cache Synchronisation Block Mobility and Migration Block Allocation Prefetching Scalability Summary Implementation Clients and Block Servers Client Block Server Locality and Client start up Communication Transport Protocol Messages Common Client and Server Communication Client Network Queue Overlapped IO Block server specific communication Synchronisation Distributed Synchronisation Internal Synchronisation Cache Cache coherency Block migration Aggressive Prefetching Issues and Challenges Summary Evaluation Test Environment Migration Scalability Effect of block size on performance NFS Comparison

7 7.6 IOZone Summary Conclusions and Future Work Summary Conclusion Future Work Potential Extensions Final Words Bibliography 62 A Performance analysis scripts 64 B NomadFS current quirks 66 C IOZone benchmark results 68 D Configuration file format for NomadFS 70 E NomadFS source code listing 71 7

8 List of Figures 2.1 A file system File system layout on block abstraction (not to scale) Inode structure including indirection blocks A distributed file system VFS flow example. A user space write system call passes through the VFS and reaches the required file system write function. Adapted from Fig [11] High Level Architecture Client to server link Block and inode identifier Message passing File based cache invalidation Client Architecture Message layout in NomadFS (Data Block not to scale) Network queueing Overlapped IO (Adapted from Figure 2.4 [12]) Synchronisation Buffer Cache (Adapted from Fig [20]) Migration flow Test Environment Migration Performance Scalability on file smaller than cache Scalability on file larger than cache Affect of block size on performance IOZone Write IOZone Random Write IOZone Read

9 7.9 IOZone Random Read

10 Acronyms API Application Programming Interface. FUSE Filesystem in Userspace. LFS Log-Structured File System. NFS Network File System. RAID Redundant Array of Individual Disks. VFS Virtual File System. 10

11 Chapter 1 Introduction Multiple computer systems such as cluster computers and computer laboratories generally have a large amount of aggregate storage, due to each machine having its own small hard disk drive. As opposed to making use of the combined storage and performance capabilities of these small disks, a common approach to shared data in these systems is to use a single centralised storage system. A distributed file system which can take advantage of these storage and performance capabilities would help to improve the usefulness of shared data in a small scale distributed settings. This report covers the design and implementation of NomadFS, a new, primarily block-based distributed file system for the Linux environment. NomadFS is aimed at meeting the needs of smaller scale distributed environments. From a user s standpoint, performance is important. Because of this NomadFS has built in functionality which allows maximal usage of the machine s local disk. This includes preferring the local disk when creating data and allowing data to migrate to the disks of machines which use it the most. So that goals such as migration could be implemented and tested, common distributed file system functionalities such as fault tolerance through replication were not deemed a priority in this research. When approaching this problem there were a number of options available on how to implement such a file system. Firstly a decision was needed on whether the underlying architecture would operate on blocks or files. A block-based approach refers to the ability for the file system to operate directly on top of a block device while a file-based approach means that the file system relies on some form of underlying file architecture. For reasons that are explained in Chapter 5, a block-based approach, with some file based elements, was chosen for NomadFS. 11

12 Chapter 2 contains background file system information. This includes a background to file systems, block-based file systems and distributed systems. An understanding of these topics is required to fully understand this project. Chapter 3 contains the set of goals which NomadFS aimed to meet. Distributed file systems are not a new topic in Computer Science, it is therefore necessary that some related implementations are surveyed. This file system survey can be found in Chapter 4. The design and implementation of NomadFS are central to this project and are covered in Chapters 5 and 6. Chapter 5 overviews the design of NomadFS, and why these design decisions were made. Chapter 6 covers the implementation, and covers the specifics of how the various design elements were implemented in NomadFS. Chapter 7 contains a performance oriented evaluation of NomadFS in its current state. Chapter 8 rounds off the report with conclusions and potential future work. 12

13 Chapter 2 Background This chapter covers the background to this project. This includes an overview of file systems and in particular block-based file systems for the unfamiliar reader. Distributed systems, distributed file systems, and some the issues they encounter are then covered. The chapter ends by covering the Linux Virtual Filesystem (VFS) and Filesystem in Userspace (FUSE) with some depth. An understanding of these topics, especially the later ones is important in the context of this project. 2.1 A file system overview A file system is software that provides a means for users to store their data in a persistent manner. From the user s point of view this is generally seen as directories and files. End User Files and directories / /file1 /directory1/ /directory1/file2 File System Block Device Figure 2.1: A file system 13

14 In file system terminology disks, or raw devices, are divided into equal sized segments called blocks. File systems are then built on top of this block-based storage abstraction, which is typically provided by a block device driver that interfaces with a piece of hardware such as a hard disk drive (HDD). For data to remain persistent, the file system must lay the data out on this series of blocks in an organised manner. Most Unix file systems do this by making use superblocks, inodes, bitmap areas, and data blocks. These are shown in Figure 2.2 and described in the following paragraphs. Superblock Inode Bitmap Data Bitmap Inode Table Data Blocks Figure 2.2: File system layout on block abstraction (not to scale) Superblock A superblock is present at the beginning of the disk at a fixed location. It provides important information about the file system that follows it. This includes such information as what type of file system it is and how large the different areas of it are. Bitmap Bitmap blocks follow the superblock and show which inodes and data blocks are currently allocated. A single bit is high if that particular inode entry or data block is allocated, and low if it is not. Naively using a bitmap means that a files data can potentially be sparse on the raw device. Directory Data 17 file.txt Inode number File name Inode Mode Direct block pointers Indirect block pointers UID File Size Metadata Data Blocks Figure 2.3: Inode structure including indirection blocks 14

15 Inode Following the bitmaps is the inode table. An inode is a data structure which contains information relating to a file. This includes metadata such as when the file was created and which user owns it. Most importantly, the inode contains where the file s data is located through the use of block pointers. Because an inode is small and fixed in size and an individual file can potentially hold many block pointers, block pointer indirection is required, as shown in Figure 2.3. The number of indirect blocks that the file system supports determines the maximum file size. It is worth noting that the file an inode represents can be a directory. In this case the file s data contains the file names of the children and the identifier of the inode associated with the child (shown in Figure 2.3). The root directory / is located at a fixed point in the inode table, and provides the starting point for directory traversal to any file or directory in the file system. The MINIX file system [20] follows this layout very closely, but obviously, such method of laying out data on a block-level abstraction is not the only way that a file system can be organised. For example, the Linux extended file system (Ext FS) divides the blocks into Block Groups, each of which contain a superblock, bitmaps and data blocks [2]. This helps to keep the data from an individual file in sequential order so as to speed up accesses on the underlying device System calls For a file system to be useful, it must be accessible by the user. POSIX [8] defines a set of system calls which provide a consistent means of accessing file system information. Almost all modern file systems including Ext conform to this POSIX standard. So that one can gain an understanding of what functionalities a file system must cater for, the following is a list of the most important POSIX file system calls. Even a simple file system must be able to handle most of these. List of important system calls system call name arguments description open file s path. Returns a file descriptor which describes the given file. This can be used in subsequent system calls. close file descriptor. Closes the open file. seek file descriptor. Sets the offset which reads or writes should operate from. 15

16 read file descriptor, buffer read into, number of bytes to read. Returns a number of bytes from the described file. write file descriptor, buffer to write from, number of bytes to write. Writes a number of bytes to the described file. truncate file descriptor, new file size Sets the size of the file. mkdir path of directory. Creates a directory. rmdir path of directory. Remove an empty directory. mknod path of file. Creates a file. unlink path to file. Remove a directory entry (e.g. a file). rename path to old file, path to new file. Rename a file. Example system calls The following is a description of how a block-based file system handles the open and read system calls from the previous list. When a user space program wishes to read a file, it must firstly open it using the open system call. The system call results in a software interrupt and eventually the file system open function is run. The file system then traverses the directory tree until the file s inode identifier is located. Using the inode s identifier as an offset into the inode table, the file system can then read the inode from the block device. The system call finally returns a file descriptor, which can be used to describe this file in any further calls on this file. To read data from the file the program then calls the read system call on the file descriptor with the number of required bytes and a buffer to read the bytes into. As with the open call, this read passes through the kernel and reaches the appropriate file system function. The file system program then reads the appropriate inode and calculates which data block is required. Using the offset into the file s data that is required, the file system can calculate which data block pointer is needed. Assuming no indirection, this pointer can then be read from the inode and then the actual block data can be read and returned to the calling program. If indirection is required (i.e. if the file s block pointers overflow onto indirect blocks), then the file system must traverse this block indirection to locate the required block. In practice there are a number of complications in this process that relate to caching and error handling. For simplicities sake these are not mentioned here. 16

17 2.2 Distributed systems Distributed systems are computer systems which cover multiple computers connected by a communication network [19]. Because they have access to the combined computational capacity of these machines, distributed systems normally have potential for greater performance and capacity. They do, however have a number of implications which result in them often becoming more complex than single computer systems. Distributed file systems are a sub-topic of distributed systems and aim to present a single usable file system to multiple client machines while making use of the storage capacity of multiple server machines. A single machine may sometimes act as both a client and a server. Such a file system has a number of advantages over single machine file systems including potential for storage more and performance due to the use of multiple storage devices. As well as these, distributed file systems also have the potential for improved reliability and an improved environment for sharing of data. This sharing of data can be particularly useful in situations that involve parallel processing [5]. End User / /file1 /directory1/ /directory1/file2 / /file1 /directory1/ /directory1/file2 / /file1 /directory1/ /directory1/file2 Client File System Client File System Client File System Remote Data Remote Data Remote Data Figure 2.4: A distributed file system The important issues and implications that relate to distributed systems [10] are listed and summarised below, all of which must be handled appropriately by any distributed system. As they are of more interest in the context of this report, the points which are particularly related to distributed file systems are covered with more depth. 17

18 2.2.1 Communication Since a distributed system exists across multiple machines there is a requirement for communication between nodes over a network. This is often achieved with message based communication, where machines communicate with a consistent messaging interface. There are, however, other paradigms including Remote Procedure Calls (RPC) Synchronisation and Consistency Synchronisation refers to the coordination of separate processes as they act on shared data. Consistency in a distributed system refers to the ability to keep data consistent between varying machines, even after changes occur. Both are related to one another and come into play when different programs use shared data structures. Because a distributed file system is inherently a single shared data structure between clients, these points are also of particular interest to them. In a distributed file system, it is essential that the system can safely handle or avoid synchronisation issues such as concurrent updates. A concurrent update can potentially result in data loss, and can be avoided using techniques such as mutual exclusion around shared data. In a distributed file system, one technique for providing synchronisation of files is to only allow a single writer or any number of readers access to a file. Consistency is often related to replication and caching because of the opportunity for data to exist in multiple locations. Having the ability to duplicate data might lead to consistency issues, but is often required so that maximum performance can be attained. To handle consistency correctly, a distributed file system will generally implement systems that allow functionality such as cache coherency Fault Tolerance With an increased number of machines, distributed systems have an increased chance of possible failure at any individual point. Because of this, the requirement for safe handling of failure in individual machines is often seen as an important aspect of distributed systems such as distributed file systems. Fault tolerance is often achieved with some level of redundancy, such as storing every piece of data on two separate machines. Although not a distributed file system Redundant Array of Individual Disks (RAID) 2 provides an example of how this 18

19 can be achieved, where a single parity disk results in the ability for any single disk to be recovered in the event of a disk failure [16] Performance Since a distributed system operates over multiple machines, it appears possible at first sight for it to perform better than a single machine. Actually achieving this performance in a distributed system is not always straight forward. Performance in file systems, including distributed file systems is, often measured by the speed at which reads and writes on files can occur. The challenge of achieving good performance is particularly related to some of the earlier points, such as synchronisation and consistency, where achieving these can often result in reduced performance. Achieving acceptable performance while also maintaining these elements can be an important challenge in any distributed system and involves designing the system so that it balances these features based on the project s goals Scalability Depending on the goals of the distributed system, it is often desirable that the system can continue to operate appropriately even when the number of machines increases. The extent to which the system is scalable should be set in the goals of any distributed system Transparency Transparency refers to the system hiding the underlying implementation from the calling program. In a distributed system this often refers to hiding the network and the presence multiple machines so that the system appears as a single machine or system. In Linux file systems this is guaranteed due to the Virtual File System (VFS), which is described in Section 2.3 below. 2.3 The Linux Virtual File System The Linux kernel supports many varying file systems, all of which have much in common. Linux achieves consistency in this functionality by providing a layer between user space file system calls and the actual kernel file system programs. This abstraction layer, or common interface, is known as the VFS (Virtual File System), an object oriented set of functions and data structures which kernel space file systems must implement and make use of [11] [1]. This means that 19

20 the kernel need not know of the underlying file system architecture. Therefore, any file system can potentially be implemented in the Linux environment. All file system operations pass through the VFS (see Figure 2.5 below), so it is important to have an understanding of what role it performs. write() sys_write() file system's write method user space VFS file system physical media Figure 2.5: VFS flow example. A user space write system call passes through the VFS and reaches the required file system write function. Adapted from Fig [11]. 2.4 Filesystem in Userspace Filesystem in Userspace (FUSE) (Filesystem in Userspace) is a Linux kernel file system module that allows a user space program to indirectly resolve file system calls. In short, the FUSE kernel module catches file system calls from the VFS and proceeds to forward them to a user space file system program to resolve. There are a number of positive and negatives to file system development in this manner. Kernel space programming is generally more difficult and time consuming than user space programming. This is not only because of less documentation of kernel space code but also due to the more time consuming development cycle, the available API functionality and the increased consequences of mistakes. Because of this, FUSE allows a faster and easier environment in which to develop file systems. This does, however, come at the cost of an increase in overhead because of need for additional memory movement and context switches [21]. Such overhead becomes less important when dealing with networked file systems and therefore distributed file systems because of the longer wait times on network operations [3] [17]. 20

21 2.5 Summary File systems provide a means for data to be stored and represented to end users. Distributed file systems extend this notion so as to include multiple machines but must also account for additional complexities. In the Linux context, the VFS provides a means of abstracting away the file system internals, while FUSE provides a means of developing user space file systems. 21

22 Chapter 3 Goals Chapter 2 covered the background of file systems and distributed file systems. Using this information it is possible to list the goals which NomadFS aimed to meet. NomadFS is aimed at providing improved performance in a smaller scale distributed setting, over centralised storage solutions. Because read operations are seen as more important, the goals are based on the use case that a single writer or n readers will act on an individual file at a given time (i.e. many readers but only a single writer can act on a file at any given time). Performs well for use case. A method of achieving this, in a distributed scenario, is to maximise the usage of the local disk. This is because the local disk is faster to access than a remote one and is further expanded upon in Chapter 5. Maximising the usage of the local disk can be achieved through the preferred use of the local server (if it exists) when creating new data and through migration of data to the local server. Allow for multiple clients to concurrently act on a file. As mentioned in Section 2.2 this means that the system must appropriately handle synchronisation and consistency issues. This includes, for example, handling of concurrent updates and consistency of client caches. How these are handled comes back to the use case of many readers but only a single writer. File system implementation should be completely transparent (i.e. the calling program doesn t know it is any different than any other file system). This is implicitly provided by the VFS, but is worth mentioning. Consistency should be guaranteed. This implies that the file system forces clients to always display the latest modifications file data and is needed if 22

23 users are to share data appropriately. Scales well over the number of machines that exist in laboratory or cluster scenarios (i.e. hundreds). This is related to the performance goal, but is separated here so that it is clear that the system should continue to perform well when machines are added. As the number of machines in a system increases so to does the chance of failure at an individual point. Because of this, distributed file systems often focus on fault tolerance mechanisms such as data replication. So that functionalities such as data migration could be explored, implemented and tested, fault tolerance issues were not covered in this research. Because of these goals, a block-based approach to implementation was chosen for NomadFS. The reasons for this are further expanded in Chapter 5, and included a natural ability for data from a single file to be split across multiple machines. This splitting of file data is important when dealing with data migration. Together, achievement of these goals results in distributed file system which allows for a well performing shared storage medium of data in a small scale distributed setting. 23

24 Chapter 4 File System Survey Distributed file systems are not a new topic in computer science. Because of this, it is important that some other distributed file system implementations are examined. This chapter examines four distributed file system examples that illustrate important aspects of distributed file systems and are also related to the goals of NomadFS. 4.1 Network File System (NFS) Implementation Because it allows for only a single server, NFS [13] [18] is not strictly a distributed file system. It is however worth mentioning because it is probably the most well known and most widely used distributed file system and is often used as a comparison in network file system benchmarks. NFS works on a file level, exporting the contents from an underlying local file system, such as Ext4, over a network connection from the NFS server. Multiple NFS clients can then concurrently connect and interact with this exported file system. NFS also offers an interesting caching system, with both the client and server maintaining a cache. The server cache performs the same function as that of a standard file system cache, aimed at reducing the number of disk accesses. The client side cache, however, aims to reduce network load. Consistency is achieved in directory and attribute information by caching for a predetermined length of time. File data on the other hand is checked for validity only on a file open. Comparison of goals with NomadFS As it provides a simple method of distributing and sharing a single file system across multiple clients, NFS is what is often used in laboratory and cluster scenarios. A central NFS server 24

25 can, however, cause bottlenecks and proves to be a single point of file system failure. NomadFS aims to distribute data across the clients, making use of both the aggregate storage potential and distributed computational capacity of the client disks. 4.2 Gluster File System (GlusterFS) Implementation Gluster File System (GlusterFS) [9] is a distributed file system which exports a local file systems from a set of servers to multiple clients. Each of these is known as a volume. Clients then access these files using either TCP/IP, InfiniBand or SDP. GlusterFS supports a number of distributed file system mechanisms such as file based replication, striping and failover. It also has mechanisms to avoid coherency problems and scales up to several petabytes. The client file system runs through the FUSE API. Comparison of goals with NomadFS It is useful to examine GlusterFS as it has similar performance goals to NomadFS, is also commonly used in smaller scale distributed settings and makes use of the FUSE API (see Chapter 6). GlusterFS also operates in a manner which does not require any centralized metadata storage location, a feature which NomadFS replicates. GlusterFS operates on a file-level and requires the use of an underlying local file system on each server. Because of this it does not allow for any block-level migration of data to the local server, an aspect of NomadFS which aims to improve performance by maximising the use of the local disk. GlusterFS was one of the motivators for NomadFS. A previous student at Waikato University attempted to use it in a small scale distributed scenario. Despite significant attempts to tune GlusterFS, its performance was not satisfactory, leading to the idea that GlusterFS is too complicated, and a simpler approach may provide better performance. 4.3 Google File System Implementation The Google File System (GoogleFS) is a proprietary, widely deployed distributed file system which provides reliability and performance on a large scale [4]. GoogleFS was designed to meet Google s large scale storage and file IO requirements. It does this by making a number of assumptions. These include file reads will occur as large streamed or small random tasks. Also assumed is that writes will generally be large sequential appends. It also 25

26 attempts to provide high sustained network bandwidth as opposed to low latency in operations. Finally the file system is assumed to be run on inexpensive commodity components and therefore must therefore must provide a reasonably amount of fault tolerance and replication of data. GoogleFS operates in chunks of file data and consists of GFS clients, GFS chunkservers and a GFS master. A GFS master node provides a mapping from file name and chunk index to a chunk location for GFS clients. Once a client has a mapping it can request the file chunk from a GFS chunkserver. These chunkservers store the chunks as individual files in an underlying file system. Because of the previous assumptions the Google File System was designed with 64 MB chunk sizes, much larger than a standard file system block (generally 4kB). Comparison of goals with NomadFS GoogleFS provides a relevant example of a distributed file system as it is a well known production system that distributes file data across machines, making use of the storage and performance capabilities of individual small disks. Because chunks can be seen as large blocks, the underlying architecture is block-based, naturally allowing for splitting of file data across multiple machines. GoogleFS, however, achieves its goals on a much larger scale than the use case for NomadFS. Because of this it can safely operate with centralised chunk organisation. In a smaller setting such as NomadFS s use case, a decentralised structure is preferred so that an individual point of failure can be avoided. 4.4 Zebra and RAID Implementation The Zebra Striped Network File System is a network or distributed file system which attempts to improve data throughput and availability by basing its design on the ideas that are present in RAID and Log-Structured File Systems (LFSs) [6] [7]. In the case of RAID, Zebra is similar in that data is striped across multiple networked devices. This means that that server load is distributed among multiple server machines whenever a client attempts to perform an operation on a file. This is particularly important in the case of slower write operations which are required to be written to the physical disk. Zebra also uses a parity scheme like many RAID schemes such as RAID 2. This parity scheme means that a single server acts as a parity server (an XOR combination of the other servers). Subsequently, in the event of an individual server failure, all data can be restored using the data present on the other servers. 26

27 LFSs, such as Zebra, perform write operations as a sequential log on the storage. This is based on the observation that, with increased memory, write operations to physical media will outnumber read operations. Hartman [7] shows that applying these techniques can provide performance benefits. In the case of large files a 4 to 5 times speed up was seen in comparison to NFS and Sprite (Zebra predecessor). Smaller files saw a speed up of between 20% and 300% times. Comparison of goals with NomadFS Zebra file system is of interest to this project and NomadFS as it is an existing implementation of a block-based distributed file system which can split data across multiple server machines. It also shows that potential benefits of making use of the data storage on multiple machines. Striping of data, as exists in Zebra, is also of interest to NomadFS, as it provides a possible piece of future work that could potentially aid performance. Zebra was, however, developed in 1994 and is not publicly available. This age means that it has performance that is not targeted at the larger, faster disks and improved network bandwidth that exist in modern computing. 4.5 Summary Many distributed file systems have been developed for varying purposes over a number of years. Examining past implementations can aid in the development of new distributed file systems such as NomadFS. NomadFS however has a different combination of features to other distributed file systems and revisits these architectures in the current context of disk and network performance. 27

28 Chapter 5 Design Chapter 2 introduced and provided background on file systems and, in particular, distributed file systems. This chapter presents the design of NomadFS, making use of the goals that were presented in Chapter 3 so as to aid design decisions. This chapter flows from high to low level design. This begins with an architecture overview, moves on to an overview of the block interface and the motivations behind it. The structure of the file system is then covered, with performance and reliability aspects covered last. Chapter 6 builds on this chapter to provide a discussion of how these design elements were implemented. 5.1 Overview Key Machine Client Client Server Client Server Server Figure 5.1: High Level Architecture From a high level point of view NomadFS consists of clients and servers. The client is a program which provides the file system call functions to the end user. The server has access to the file system data. Clients communicate with servers 28

29 over a network such as LAN. Collectively the servers form the distributed file system s data, with the client connecting, collating the data and exporting it back to local end users in the file system format. As can be seen in Figure 5.1 clients and servers can potentially exist on the same machine. Communication in these cases is identical to that which occurs over the machine to machine network, but occurs over the internal local link. This local link is dedicated and does not experience variable network conditions like the shared network and because of which is generally faster. In terms of performance it is therefore of paramount priority that the usage of this local link is maximised. 5.2 Block interface The primary focus of the server is to serve blocks over the network to the clients from a block device. Because of this the report may refer to the servers as block servers. Communication is performed over a common network API or block interface, where clients request blocks over the API and servers respond to these requests with the appropriate data block. Client Request Blocks Export Blocks Server Communication API Figure 5.2: Client to server link Block-based approach A block-based approach to development for NomadFS was chosen for a number of reasons. Firstly such an approach is of interest so as to provide an indication as to whether it is a viable option for a distributed file system. It was however believed to be the case for a number of reasons. A block-based approach allows for a natural approach for dividing data from a single file across multiple machines or devices. What this means is that, any individual block from a file can potentially exist at any location in the entire system. Achieving this functionality in a file system which deals with files, rather than blocks would be more difficult and would rely on entire file based migration, or an extension to the file semantics (because a file is inherently a 29

30 single chunk of data). Having the ability to easily divide data across multiple devices also has the benefit of allowing for block mobility. Because a block-based file system is designed to directly interact with a raw device, such an approach to a distributed file system has no reliance on an underlying host file system. What this means is that, instead of being forced to serve data that exists in a local file system, as is the case in existing file systems such as NFS, a block-based distributed file system can directly use raw devices, such as individual disk partitions. Whether this is actually a performance benefit remains to be seen however. Finally a block-based approach allows for simplicity in a number of key areas. This mostly includes the block servers, which as mentioned have the primary task of serving blocks, a simple task in user space Linux programming Identification and locality For blocks to be accessible in a distributed file system, there needs to be a method of identifying which machine contains the block. Each block and inode has a 64 bit identifier which includes the server where the block or inode is located, and the index into the block device that the block or inode is located. This layout can bee seen in Figure 5.3. Because of the block-based nature of NomadFS, the block pointers that contain these identifiers can be modified, allowing for mobility of individual blocks. Such an identifier structure also allows a large number of blocks to exist in the system (up to 2 48 blocks on each of 2 16 servers). A block s identifier is stored directly in the inode or one of the inode s indirect blocks. An inode s identifier is stored in the parent directory s data blocks, along with the file s name. Server 16 bits Block / inode offset 48 bits Figure 5.3: Block and inode identifier Clients can therefore identify which server to request a block or inode from. 5.3 File system structure A distributed file system such as NomadFS involves a number of complexities which do not exist in a local file system, such as Linux s extended file system. This stems from the need for suitable communication over a network, 30

31 and the need for appropriately handling multiple machines operating on shared data. Communication not only needs to be reliable, but also has to perform in a suitable manner if the system is to achieve its performance goals. Reliable communication leads on to the need for appropriate synchronisation of the shared data structures, something that is often achieved with locking or mutual exclusion. These points are further expanded upon in this, and the following sections Communication API The client and server communicate using a common network API, where communication is performed using messages. A messaging approach was chosen because it fits well with the block-based approach, where a single message can be used to hold the data from a single block. A message contains a request or a response. Requests include such operations as a read request and are responded to by the block server with a read response. While the request in this case is small and only includes the required block identifier, the response is slightly larger than the block size. Request Message Client Server Response Message Figure 5.4: Message passing So as to maximise the effective use of the local link, the client creates inodes and allocates blocks on the local server (if it exists) by preference. Extending Communication Some operations such as inode and bitmap operations are performed on partial blocks. The block interface is not ideal for performing these operations because of both synchronisation and performance issues. The synchronisation issues stem from the need for locking of the entire block or part of the block. Locking the entire block is wasteful because it would stop other operations occurring elsewhere on the entire block for the duration of the lock. Locking of partial blocks would work but would add a large amount of complexity to the system. Performance issues of partial block operates are the result of wasted network 31

32 network traffic. Using the block interface would mean that entire blocks of data are transferred when only part of the block is actually required. In NomadFS s case, the solution to these issues is to implement these partial block operations on the server. This required the communication API to be extended so as to include a number of additional operations such as allocating blocks and freeing inodes. Having the ability to extend the API also means that other non-block operations can be performed server side. This includes such functions as leasing and reference counting, which are discussed in Section 5.4. Extending the API so that some file system operations occur on the server does however have some drawbacks. Most importantly it adds complexity to the server, which now needs to have the ability to perform low level bitmap and inode operations. Overall it strikes an appropriate balance between performance, while still mostly holding to a block-based nature. It also means that synchronisation of these partial block operations can be provided completely server-side (see Section 5.4.2). Example Messages To illustrate how this communication API operates, some of the common operations are listed below, each of which is sent in a single message, and has an appropriate response message from the server. Data block read Data block write Inode create Inode free Allocate block Free block Actual implementation details of this communication is shown in Section 6.2, and includes aspects such as how reliability and performance of communication were implemented. 5.4 Performance and Reliability This section describes the additional features of NomadFS that are particularly related to performance and reliability. This includes such aspects as how the 32

33 cache operates, how synchronisation was achieved and why block migration was implemented Cache For a file system to perform in a suitable manner, it will have to make use of a memory cache. This is so that data can be fetched from memory, as opposed to the slower disk subsystem, and is particularly important in network file systems such as NomadFS [6]. Generally in a standard single client-device scenario, caching coherency does not prove to be an issue because the file system can assume that cached data has not changed on the block device. In a distributed file system however, because there are multiple clients potentially modifying the shared files, cached data can become out of date. It is therefore necessary that such a system has a mechanism for invalidation of this cached data. Cache coherency through invalidation In a file system such as NomadFS cache invalidation could occur in either a block oriented or file oriented manner. While a block approach would suit the block-based nature of NomadFS, blocks are small and because of which would add a large amount of overhead due to the potential for large numbers of cached blocks. On the other hand, a file based approach has the problem that even when only a small part of a file changes, the entire file s cached data would need to be invalidated. This does however mean that invalidations do not require very much network traffic and state storage. NomadFS achieves cache coherency between clients using a method of file based cache invalidation (see Figure 5.5). This means that when a file is modified, all clients that have data from this file cached are notified of the modification. Upon receiving this notification, the client can invalidate the appropriate cached inodes and data blocks. The next time a system call requests data from this file, the client will have no cached data from this file and will fetch the data directly from the appropriate server. For this to work the server which holds the file s inode needs to track which clients have some of the file s data cached. The client also needs to keep a record of which blocks belong to which inode. This file based cache invalidation was appropriate as it provides an acceptable balance between unnecessary cache data loss and potential invalidation overhead. 33

34 Key Cached Data Client Client file.txt 1 Write to file.txt 2 Invalidate file.txt Client Server Client file.txt 2 Invalidate file.txt Figure 5.5: File based cache invalidation Synchronisation Any program which has multiple threads of execution potentially modifying shared data must make sure to synchronise actions so as to avoid concurrent update errors. In the case of NomadFS, from a high level viewpoint, these threads of execution are individual clients, which can potentially concurrently access and modify the shared file data simultaneously. Synchronisation is often achieved using write locking of data so that only a single thread, or in this case client can write to that data at a single point in time. Synchronisation in NomadFS was achieved in a number of steps. As mentioned in Section 5.3.1, partial block operations, such as inode and bitmap operations occur on the server. Because of this, synchronisation of these operations is already achieved, and merely involves internal server synchronisation. Partial block operations are not the only operations that require synchronisation. Anything that the client does will require some scheme that will provide consistent, non-colliding data access and modification. There are a number of options available that could provide this functionality, including implementation of block locking or leasing, or even implementation of file based leasing. In NomadFS an inode level leasing method was chosen. This was implemented in a manner which allows for explicitly one writer or n readers to operate on an 34

35 individual file. Unix file system semantics state that: any number of readers and writers can act on a file at any given time, and that a read always returns the most recently written data, and that writes are always indivisible even if there are other writers. Because of this fixed number of readers and writers, NomadFS does cause a weakening of these semantics. Taking into account the use case, this was deemed acceptable. When a client begins writing to a file it requests a lease for this file s inode from the server which holds the inode. Assuming no other client holds the lease, the server can then respond with a success message. While holding this lease the client can then proceed to perform its write operation and release the lease when finished. This means that multiple writers can have a file open at any given time, however only one write operation is allowed at a time. Also, because of the possibility of changes to block pointers within the period of a write operation, reads are not allowed during such a time. Leasing is also advantageous over any form of locking as it safely handles client failures (if a client fails, the lease will expire on timeout). This high level description of synchronisation ignores internal synchronisation requirements of shared data between a client or server process s threads. These are covered in Chapter Block Mobility and Migration One method for improving performance is to implement the ability for data to be dynamically migrated between machines, so as to reduce the latency and general throughput in repeat transactions. Such functionality is particularly important when dealing with files that are unable to be cached for the entire duration they are used at a particular client. For example when the file is larger than available memory or is repeatedly accessed over a long period of time. As mentioned in Section 5.2.2, because of NomadFS s block-based nature, individual blocks are potentially mobile. Because of this NomadFS implements block migration of data blocks on read. This means that while a file is read by a client, the blocks that exist on remote servers are migrated to the local server. Subsequent reads from the server can then be fetched from the faster local server Block Allocation In an early revision of NomadFS it was noted that write operations performed poorly. After investigation this was found to be the result of not the write 35

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