Frangipani: A Scalable Distributed File System
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Frangipani: A Scalable
Distributed File System
C. A. Thekkath, T. Mann, and E. K. Lee
Systems Research Center
Digital Equipment Corporation
Presented by: Long Zhang
Slides come from the combination of previous course
and Frangipani’s original slides in SOSP 97Motivation
Large-scale distributed file systems
are hard to administer
Administration is a problem because of
- size of installation
- number of components
2Outline Background Introduction System Structure Disk Layout Logging and Recovery The Lock Service Easy Administration Performance Conclusions Questions
Background (cont'd)
Original slides: http://ftp.digital.com/pub/Digital/SRC/
publications/thekkath/talk/frangipani-sosp.ppt
This paper is built on top of two related papers:
Edward K. Lee , Chandramohan A. Thekkath, Petal: distributed
virtual disks, Proceedings of the seventh international
conference on Architectural support for programming
languages and operating systems, p.84-92, October, 1996,
Cambridge, Massachusetts, United States.
Leslie Lamport. The Part-Time Parliament. Technical Report 49,
Digital Equipment Corporation, Systems Research Center,
130Lytton Ave., Palo Alto, CA943011044, September 1989.Related Work
NFS (Sandberg et al.,’85, SUN)
VAXClusters (Kronenberg, Levy, & Strecker,’86,
DEC)
AFS (Howard et al.,’88, CMU)
Echo (Mann et al.,’94, SRC)
xFS (Anderson et al.,’95, Berkeley)
Calypso (Devarakonda, Kish, and Mohindra,’95,
IBM)
Shillner and Felten (’96, Princeton)
5Introduction
Many distributed file systems already there:
VMS Cluster file system, Echo, Calypso, and etc.
Generally, large-scaled distributed file
systems are hard to manage. Lots of file systems
administration work require human intervention
– have to be done manually.
The administration problem is caused by
Growing computer installation.
More disks attached to more machines.
(components)Introduction – Solution
Frangipani
A new scalable distributed file system.
Two layered model: build on top of Petal, a
distributed storage system.
Can also be viewed as a cluster file system.
It can solve the administration problem by
Give all users a consistent view of files.
Frangipani servers can be easily added to existing
installation to improve the performance.
Add users without manually configuration.
Dynamic/hot backup support
Fault tolerance. (machine, network, disk failures)Petal Prototype
Petal Petal Petal Petal
Client Client Client Client
Switched Network
Petal Server Petal Server Petal Server
Disk Disk Disk
s s s
Petal virtual
disk
8Introduction – Layered structure
User User User
program program program
Frangipani file Frangipani file
server server
Distributed Petal
lock service distributed
virtual
Physical disksSystem Structure – Common
workstations
Petal virtual diskSystem Structure – Components
User programs access Frangipani through the
standard operating system call interface. (Digital
Unix vnode interface)
Frangipani file server module runs within OS
kernel.
Changes to file contents are staged through the
local kernel buffer pool. Could be volatile until
next fsync/sync system call.
Metadata changes are logged in Petal and be
guaranteed non-volatile. (Write ahead redo log,
discuss later)Components Frangipani file server module read/write Petal virtual disks using local Petal device driver. Exploit Petal’s large virtual space. More details in a separate paper. The lock services Multi-reader/single-writer lock Lock with leases (discuss later)
Client/Server configuration
Security issues:
Any Frangipani machine can read/write any block of
the shared Petal virtual disk.
Eavesdropping on the network interconnecting the
Petal and Frangipani machines
Solution: run Frangipani, Petal and lock servers on
trusted network, machines and OSs .
Client/Server configuration.
All the servers are interconnecting with a private
network.
Remote, untrusted clients talk to Frangipani servers
through a separate network. (have no access to Petal)
Bonus: Clients can use Frangipani without modifyingClient/Server configuration
System Structure – Design issues
Why not use an old file system on Petal?
Petal works with old file systems.
Traditional file systems such as UFS, AdvFS (target
in performance section) cannot share a block
device.
The machine runs the file system can be a
bottleneck.
Why choose two layer structure?
Two layer structure is not unique. e.g. Universal
File Server.
Modularity. Frangipani machines can be added
and deleted transparently.
Consistent backup without halting the system.Design issues (cont'd)
Three aspects of the Frangipani design can be
problematic:
Duplicated logging. Sometimes logged both by
Petal and Frangipani.
Doesn’t use disk location information in placing
data.
Frangipani locks entire files and directories rather
than blocks.Disk Layout
264 bytes of address space provided by Petal
Commits/decommits in large chunks – 64K
Six regions in address space:
1st region stores shared configuration parameters
and housekeeping information – 1TB
2nd region stores logs. Each Frangipani server has
one. Reserved 1TB, partitioned into 256 logs.
3rd region is used for allocation bitmaps, to
describe which blocks in the remaining regions
are free – 3TB
4th region holds inodes. 1 TB inode space, eachDisk Layout (cont'd)
5th region hold small data blocks, each 4KB in
size. Allocated 7TB
The remainder holds for large data blocks. 1 TB
for each large block. 224 large files limit.
Frangipani takes advantage of Petal’s large,
sparse disk address space to simplify its data
structure.Logging and Recovery
Frangipani uses a write ahead redo log for
metadata
Metadata: any on-disk data structure other than
the content of an ordinary file.
Log records are kept on Petal.
Logs are bounded in size – 128 KB
Data is written to Petal
On fsync/sync system calls, or every 30 seconds.
On lock revocation or then the log wraps.
Each Frangipani machine has a separate log
Reduces contention
Independent recoveryLogging and Recovery (cont'd)
Frangipani server crashes can be detected in two
ways:
Detected by a client of failed server;
When the lock service asks the failed server to
return a lock it is holding.
Generally, recovery is initiated by the lock
service.
Recovery demon will take the ownership of the
failed server’s logs and locks.
After recovery, releases all the locks and frees the
logs.Lock Services
Multiple reader/single writer lock mechanism
Read lock allows a server to read data and cache
it.
Write lock allows a server to read or write data .
When a write lock is downgraded or released, the
server must flush its dirty data to disk.
Locks are moderately coarse-grained
Lock for each logical segments
Each file, directory or symbolic link is one segment.
protects entire file or directoryLock Services (cont'd)
Avoiding deadlock by globally ordering these
locks.
And acquiring these locks in two phases:
A server determine what locks it needs. Which file
or directory? Read lock or write lock?
The server sorts the locks by inode address and
acquires each lock in turn.
Then checks whether any objects identified in phase
one were modified while their locks were released. If
so, the server releases locks and loops back to phase
one.Lock Services (cont'd)
The lock service deal with client failure using
leases
Client obtain a lease together with the lock. If the
lease expires, the client either renew the lease or
the lock will become invalid.
Three different implementations: (Key problem:
where to store the lock state?)
1st : A single, centralized server. All lock states are
keep in the server volatile memory.
2nd: Primary/backup server. Store the lock state on
a Petal virtual disk, so in case of server crash, the
lock state can be recovered. Poor performance.Lock Services (cont'd)
3rd and final: A set of mutually cooperating lock
servers, and a clerk module linked into each
Frangipani server. Result: fully distributed for fault
tolerance and scalable performance.
Highlights of final implementation:
The lock servers maintain a lock table for each
Frangipani server. Clerk module is responsible for
communications. (via asynchronous messages)
A small amount of global state information is
replicated across all lock servers using Lamport’s
Paxos algorithm. (Also used in Google chubby
lock service http://labs.google.com/papers/
chubby.html)Easy Administration (adding/removing servers) Adding another Frangipani server requires a minimal amount of administrative work: Which Petal virtual disk to use And where to find lock service. Removing a Frangipani server is even easier. Simply shut the server off. Lock servers will invalid the locks hold by the server after the lease expired and initiate recovery service to run the redo logs.
Easy Administration – backup
Petal’s snapshot feature provides a convenient
way to make consistent full dump of a
Frangipani file system
Uses copy-on-write techniques
Crash consistent: a snapshot reflects a coherent
state.
Backup a Frangipani file system:
Taking a Petal snapshot.
And copying it to tape.Performance – Experimental Non-volatile memory (NVRAM) Solved Frangipani server latency problems. Placed in between physical disks and Petal server. Ideal testbed: 100 Petal nodes. (small array controllers) 50 Frangipani servers. (typical workstations) Reality: 7 333Mhz DEC Alpha 500 5/333 as Petal servers. Each has 9 DIGITAL RZ29 disks, 4.3 GB each. Connected to 24 port ATM switch 155 Mbit/s link.
Single Machine Performance
Why AdvFS?
Significantly faster than BSD-derived UFS file
system.
Can stripe files across multiple disks.
Uses a write-ahead log like Frangipani.
Frangipani FS doesn’t use local disks while
AdvFS using locally attached disks.
For MAB, unmount file system at end of each
phase. Same reason as the tests performed for
log-based FS.Single Machine Performance
Table 1: Modified Andrew Benchmark with unmount
operations
Table 2: Frangipani Throughput and CPU UtilizationScaling
Frangipani Scaling on Modified Andrew
Benchmark
60
Elapsed time (secs)
45 Compile
Scan
Stat
30 Copy
Create
15
0
1 2 3 4 5 6
Frangipani MachinesScaling (cont'd)
Frangipani scaling on Uncached Read
throughput(MB/s) 70.0
52.5
35.0
17.5
0
1 2 3 4 5 6
Frangipani MachinesScaling (cont'd)
Frangipani scaling on write.
throughput(MB/s) 70.00
56.25
42.50
28.75
15.00
1 2 3 4 5 6
Frangipani MachinesDiscussion
I am bit worried about its locking granularity. What if we
can lock individual blocks rather than files or directories ?
How would affect the overall performance of the system ?
Petal is using data replication for high availability.
Maintaining consistency among of several copies in a
distributed system is inherently difficult so how does Petal
deal with this issue ?
33Conclusions
Frangipani is feasible to build because of its two-
layer structure.
all shared state is on a Petal disk
easy to add, delete, and recover servers
Frangipani servers do not communicate with each
other: simple to design, implement, debug, and test
Frangipani performance is comparable to a
productions DIGITAL Unix file system (AdvFS).
Still in early prototype stage, need more experience
to improve scalability, finer-grained locking and etc.
Applications:
Design of Compaq’s VersaStore products
predates many of the storage and NAS appliances in
the industry today.Discussions
During logging and recovery, each entry in the log is given
a monotonically increasing sequence number and each
log record has a version number for the block it updates.
These are used to signify the end of the log (if the next
entry is less than the current one), or an old block (if the
block number in the record is less than the on disk version
number). However, these numbers have to be
implemented as some sort of integers in the system. How
is overflow of these taken care of? I realise that this would
take an unusually high number of writes, but wouldn't this
potentially be an issue otherwise?
35Discussions
Petal optionally replicates data for high availability. How
does this affect the locking and synchronization? When a
certain file is to be updated, are it's inodes and data
blocks simultaneously locked and updated on all the Petal
servers on which it exists? Also, since Petal can continue
functioning as long as a single disk containing the file is
available, isn't it possible that there will be inconsistent
versions of the file if any of the servers with replicated
data is unavailable at any time? How are files merged in
such situations?
36 What is the benefit of using Petal to build Frangipani? And
what is the benefit of using "so-called" virtual disk to
provide a large address space?
Do you think implementing a cluster file system on top of a
disk-based storage structure, like Petal, is better than
implementing directly on top of the file systems of an
operating system?
The bottleneck of such a system seems to be the network
bandwidth, the Petal server throughput and its disk access
time. So why does it need to implement Frangipani as an
operating system module, which both reduce the reliability
and portability? Implementing it in the user level seems
37 4) Do you think it is a balanced architecture? The
Frangipani server deals with the requests of clients and its
disk only acts as the cache. So it seems the Frangipani
server needs small disk, but fast CPU and network
interface. While, the Petal server needs large disks and
even fast network interface.
5) The system still needs manual administration when
adding/removing the either Frangipani server or Petal
server. Do you think it scales well?
38 "Only metadata is logged, not user data, so a user has no
guarantee that the file system state is consistent from his
point of view after a failure.” Is it acceptable for the users’
data to be inconsistent after a failure and any existing
distributed file system solve this problem well?
The chunk size in Petal virtual disk is 64kb, yet in the
filesytem, Frangipani, there are 4kb block and 512b inode,
that means some file operation will wait for others, right?
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