CS307 Operating Systems

Distributed-File Systems

Guihai ChenDepartment of Computer Science and Engineering

Shanghai Jiao Tong University

Fall 2011

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Chapter 17 Distributed-File Systems

Background

Naming and Transparency

Remote File Access

Stateful versus Stateless Service

File Replication

An Example: AFS

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Chapter Objectives

To explain the naming mechanism that provides location transparency and independence

To describe the various methods for accessing distributed files

To contrast stateful and stateless distributed file servers

To show how replication of files on different machines in a distributed file system is a useful redundancy for improving availability

To introduce the Andrew file system (AFS) as an example of a distributed file system

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Background

Distributed file system (DFS) – a distributed implementation of the classical time-sharing model of a file system, where multiple users share files and storage resources

A DFS manages set of dispersed storage devices

Overall storage space managed by a DFS is composed of different, remotely located, smaller storage spaces

There is usually a correspondence between constituent storage spaces and sets of files

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DFS Structure

Service – software entity running on one or more machines and providing a particular type of function to a priori unknown clients

Server – service software running on a single machine

Client – process that can invoke a service using a set of operations that forms its client interface

A client interface for a file service is formed by a set of primitive file operations (create, delete, read, write)

Client interface of a DFS should be transparent, i.e., not distinguish between local and remote files

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Naming and Transparency

Naming – mapping between logical and physical objects

Multilevel mapping – abstraction of a file that hides the details of how and where on the disk the file is actually stored

A transparent DFS hides the location where in the network the file is stored

For a file being replicated in several sites, the mapping returns a set of the locations of this file’s replicas; both the existence of multiple copies and their location are hidden

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Naming Structures

Location transparency – file name does not reveal the file’s physical storage location

Location independence – file name does not need to be changed when the file’s physical storage location changes

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Naming Schemes — Three Main Approaches

Files named by combination of their host name and local name; guarantees a unique system-wide name

Attach remote directories to local directories, giving the appearance of a coherent directory tree; only previously mounted remote directories can be accessed transparently

Total integration of the component file systems

A single global name structure spans all the files in the system

If a server is unavailable, some arbitrary set of directories on different machines also becomes unavailable

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Remote File Access

Remote-service mechanism is one transfer approach

Reduce network traffic by retaining recently accessed disk blocks in a cache, so that repeated accesses to the same information can be handled locally

If needed data not already cached, a copy of data is brought from the server to the user

Accesses are performed on the cached copy

Files identified with one master copy residing at the server machine, but copies of (parts of) the file are scattered in different caches

Cache-consistency problem – keeping the cached copies consistent with the master file

Could be called network virtual memory

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Cache Location – Disk vs. Main Memory

Advantages of disk caches

More reliable

Cached data kept on disk are still there during recovery and don’t need to be fetched again

Advantages of main-memory caches:

Permit workstations to be diskless

Data can be accessed more quickly

Performance speedup in bigger memories

Server caches (used to speed up disk I/O) are in main memory regardless of where user caches are located; using main-memory caches on the user machine permits a single caching mechanism for servers and users

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Cache Update Policy

Write-through – write data through to disk as soon as they are placed on any cache

Reliable, but poor performance

Delayed-write – modifications written to the cache and then written through to the server later

Write accesses complete quickly; some data may be overwritten before they are written back, and so need never be written at all

Poor reliability; unwritten data will be lost whenever a user machine crashes

Variation – scan cache at regular intervals and flush blocks that have been modified since the last scan

Variation – write-on-close, writes data back to the server when the file is closed

Best for files that are open for long periods and frequently modified

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CacheFS and its Use of Caching

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Consistency

Is locally cached copy of the data consistent with the master copy?

Client-initiated approach

Client initiates a validity check

Server checks whether the local data are consistent with the master copy

Server-initiated approach

Server records, for each client, the (parts of) files it caches

When server detects a potential inconsistency, it must react

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Comparing Caching and Remote Service

In caching, many remote accesses handled efficiently by the local cache; most remote accesses will be served as fast as local ones

Servers are contracted only occasionally in caching (rather than for each access)

Reduces server load and network traffic

Enhances potential for scalability

Remote server method handles every remote access across the network; penalty in network traffic, server load, and performance

Total network overhead in transmitting big chunks of data (caching) is lower than a series of responses to specific requests (remote-service)

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Caching and Remote Service (Cont.)

Caching is superior in access patterns with infrequent writes With frequent writes, substantial overhead incurred to overcome cache-

consistency problem

Benefit from caching when execution carried out on machines with either local disks or large main memories

Remote access on diskless, small-memory-capacity machines should be done through remote-service method

In caching, the lower intermachine interface is different form the upper user interface

In remote-service, the intermachine interface mirrors the local user-file-system interface

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Stateful File Service

Mechanism

Client opens a file

Server fetches information about the file from its disk, stores it in its memory, and gives the client a connection identifier unique to the client and the open file

Identifier is used for subsequent accesses until the session ends

Server must reclaim the main-memory space used by clients who are no longer active

Increased performance

Fewer disk accesses

Stateful server knows if a file was opened for sequential access and can thus read ahead the next blocks

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Stateless File Server

Avoids state information by making each request self-contained

Each request identifies the file and position in the file

No need to establish and terminate a connection by open and close operations

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Distinctions Between Stateful and Stateless Service

Failure Recovery

A stateful server loses all its volatile state in a crash

Restore state by recovery protocol based on a dialog with clients, or abort operations that were underway when the crash occurred

Server needs to be aware of client failures in order to reclaim space allocated to record the state of crashed client processes (orphan detection and elimination)

With stateless server, the effects of server failure sand recovery are almost unnoticeable

A newly reincarnated server can respond to a self-contained request without any difficulty

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Distinctions (Cont.)

Penalties for using the robust stateless service:

longer request messages

slower request processing

additional constraints imposed on DFS design

Some environments require stateful service

A server employing server-initiated cache validation cannot provide stateless service, since it maintains a record of which files are cached by which clients

UNIX use of file descriptors and implicit offsets is inherently stateful; servers must maintain tables to map the file descriptors to inodes, and store the current offset within a file

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File Replication

Replicas of the same file reside on failure-independent machines

Improves availability and can shorten service time

Naming scheme maps a replicated file name to a particular replica

Existence of replicas should be invisible to higher levels

Replicas must be distinguished from one another by different lower-level names

Updates – replicas of a file denote the same logical entity, and thus an update to any replica must be reflected on all other replicas

Demand replication – reading a nonlocal replica causes it to be cached locally, thereby generating a new nonprimary replica

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An Example: AFS

A distributed computing environment (Andrew) under development since 1983 at Carnegie-Mellon University, purchased by IBM and released as Transarc DFS, now open sourced as OpenAFS

AFS tries to solve complex issues such as uniform name space, location-independent file sharing, client-side caching (with cache consistency), secure authentication (via Kerberos)

Also includes server-side caching (via replicas), high availability

Can span 5,000 workstations

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ANDREW (Cont.)

Clients are presented with a partitioned space of file names: a local name space and a shared name space

Dedicated servers, called Vice, present the shared name space to the clients as an homogeneous, identical, and location transparent file hierarchy

The local name space is the root file system of a workstation, from which the shared name space descends

Workstations run the Virtue protocol to communicate with Vice, and are required to have local disks where they store their local name space

Servers collectively are responsible for the storage and management of the shared name space

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ANDREW (Cont.)

Clients and servers are structured in clusters interconnected by a backbone LAN

A cluster consists of a collection of workstations and a cluster server and is connected to the backbone by a router

A key mechanism selected for remote file operations is whole file caching

Opening a file causes it to be cached, in its entirety, on the local disk

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ANDREW Shared Name Space

Andrew’s volumes are small component units associated with the files of a single client

A fid identifies a Vice file or directory - A fid is 96 bits long and has three equal-length components:

volume number

vnode number – index into an array containing the inodes of files in a single volume

uniquifier – allows reuse of vnode numbers, thereby keeping certain data structures, compact

Fids are location transparent; therefore, file movements from server to server do not invalidate cached directory contents

Location information is kept on a volume basis, and the information is replicated on each server

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ANDREW File Operations

Andrew caches entire files form servers

A client workstation interacts with Vice servers only during opening and closing of files

Venus – caches files from Vice when they are opened, and stores modified copies of files back when they are closed

Reading and writing bytes of a file are done by the kernel without Venus intervention on the cached copy

Venus caches contents of directories and symbolic links, for path-name translation

Exceptions to the caching policy are modifications to directories that are made directly on the server responsibility for that directory

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ANDREW Implementation

Client processes are interfaced to a UNIX kernel with the usual set of system calls

Venus carries out path-name translation component by component

The UNIX file system is used as a low-level storage system for both servers and clients

The client cache is a local directory on the workstation’s disk

Both Venus and server processes access UNIX files directly by their inodes to avoid the expensive path name-to-inode translation routine

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ANDREW Implementation (Cont.)

Venus manages two separate caches:

one for status

one for data

LRU algorithm used to keep each of them bounded in size

The status cache is kept in virtual memory to allow rapid servicing of stat() (file status returning) system calls

The data cache is resident on the local disk, but the UNIX I/O buffering mechanism does some caching of the disk blocks in memory that are transparent to Venus

CS307 Operating Systems

Distributed Coordination

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Chapter Objectives

To describe various methods for achieving mutual exclusion in a distributed system

To explain how atomic transactions can be implemented in a distributed system

To show how some of the concurrency-control schemes discussed in Chapter 6 can be modified for use in a distributed environment

To present schemes for handling deadlock prevention, deadlock avoidance, and deadlock detection in a distributed system

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Event Ordering

Happened-before relation (denoted by )

If A and B are events in the same process, and A was executed before B, then A B

If A is the event of sending a message by one process and B is the event of receiving that message by another process, then A B

If A B and B C then A C

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Relative Time for Three Concurrent Processes

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Implementation of

Associate a timestamp with each system event

Require that for every pair of events A and B, if A B, then the timestamp of A is less than the timestamp of B

Within each process Pi a logical clock, LCi is associated

The logical clock can be implemented as a simple counter that is incremented between any two successive events executed within a process

Logical clock is monotonically increasing A process advances its logical clock when it receives a message whose

timestamp is greater than the current value of its logical clock

If the timestamps of two events A and B are the same, then the events are concurrent

We may use the process identity numbers to break ties and to create a total ordering

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Distributed Mutual Exclusion (DME)

Assumptions

The system consists of n processes; each process Pi resides at a different processor

Each process has a critical section that requires mutual exclusion

Requirement

If Pi is executing in its critical section, then no other process Pj is executing in its critical section

We present two algorithms to ensure the mutual exclusion execution of processes in their critical sections

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DME: Centralized Approach

One of the processes in the system is chosen to coordinate the entry to the critical section

A process that wants to enter its critical section sends a request message to the coordinator

The coordinator decides which process can enter the critical section next, and its sends that process a reply message

When the process receives a reply message from the coordinator, it enters its critical section

After exiting its critical section, the process sends a release message to the coordinator and proceeds with its execution

This scheme requires three messages per critical-section entry:

request

reply

release

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DME: Fully Distributed Approach

When process Pi wants to enter its critical section, it generates a new timestamp, TS, and sends the message request (Pi, TS) to all other processes in the system

When process Pj receives a request message, it may reply immediately or it may defer sending a reply back

When process Pi receives a reply message from all other processes in the system, it can enter its critical section

After exiting its critical section, the process sends reply messages to all its deferred requests

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DME: Fully Distributed Approach (Cont.)

The decision whether process Pj replies immediately to a request(Pi, TS) message or defers its reply is based on three factors:

If Pj is in its critical section, then it defers its reply to Pi

If Pj does not want to enter its critical section, then it sends a reply immediately to Pi

If Pj wants to enter its critical section but has not yet entered it, then it compares its own request timestamp with the timestamp TS

If its own request timestamp is greater than TS, then it sends a reply immediately to Pi (Pi asked first)

Otherwise, the reply is deferred

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Desirable Behavior of Fully Distributed Approach

Freedom from Deadlock is ensured

Freedom from starvation is ensured, since entry to the critical section is scheduled according to the timestamp ordering

The timestamp ordering ensures that processes are served in a first-come, first served order

The number of messages per critical-section entry is

2 x (n – 1)

This is the minimum number of required messages per critical-section entry when processes act independently and concurrently

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Three Undesirable Consequences

The processes need to know the identity of all other processes in the system, which makes the dynamic addition and removal of processes more complex

If one of the processes fails, then the entire scheme collapses

This can be dealt with by continuously monitoring the state of all the processes in the system

Processes that have not entered their critical section must pause frequently to assure other processes that they intend to enter the critical section

This protocol is therefore suited for small, stable sets of cooperating processes

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Token-Passing Approach

Circulate a token among processes in system

Token is special type of message

Possession of token entitles holder to enter critical section

Processes logically organized in a ring structure

Unidirectional ring guarantees freedom from starvation

Two types of failures

Lost token – election must be called

Failed processes – new logical ring established