Distributed Systems Principles and Paradigms Chapter 11 (version - PDF document

Distributed Systems Principles and Paradigms Chapter 11 (version October 15, 2007 ) Maarten van Steen Vrije Universiteit Amsterdam, Faculty of Science Dept. Mathematics and Computer Science Room R4.20. Tel: (020) 598 7784 E-mail:steen@cs.vu.nl, URL: www.cs.vu.nl/ ∼ steen/ 01 Introduction 02 Architectures 03 Processes 04 Communication 05 Naming 06 Synchronization 07 Consistency and Replication 08 Fault Tolerance 09 Security 10 Distributed Object-Based Systems 11 Distributed File Systems 12 Distributed Web-Based Systems 13 Distributed Coordination-Based Systems 00 – 1 /

Distributed File Systems General goal: Try to make a file system transparently available to remote clients. Client Server Requests from File stays client to access remote file on server Remote access model 1. File moved to client Client Server Old file New file 2. Accesses are 3. When client is done, done on client file is returned to server Upload/download model 11 – 1 Distributed File Systems/11.1 Architecture

Example: NFS Architecture NFS is implemented using the Virtual File System abstraction, which is now used for lots of different op- erating systems: Client Server System call layer System call layer Virtual file system Virtual file system (VFS) layer (VFS) layer Local file Local file NFS client NFS server system interface system interface RPC client RPC server stub stub Network Essence: VFS provides standard file system inter- face, and allows to hide difference between accessing local or remote file system. Question: Is NFS actually a file system? 11 – 2 Distributed File Systems/11.1 Architecture

NFS File Operations Oper. v3 v4 Description Create Yes No Create a regular file Create No Yes Create a nonregular file Link Yes Yes Create a hard link to a file Symlink Yes No Create a symbolic link to a file Mkdir Yes No Create a subdirectory Mknod Yes No Create a special file Rename Yes Yes Change the name of a file Remove Yes Yes Remove a file from a file system Rmdir Yes No Remove an empty subdirectory Open No Yes Open a file Close No Yes Close a file Lookup Yes Yes Look up a file by means of a name Readdir Yes Yes Read the entries in a directory Readlink Yes Yes Read the path name in a symbolic link Getattr Yes Yes Get the attribute values for a file Setattr Yes Yes Set one or more file-attribute values Read Yes Yes Read the data contained in a file Write Yes Yes Write data to a file Question: Anything unusual between v3 and v4? 11 – 3 Distributed File Systems/11.1 Architecture

Cluster-Based File Systems Observation: When dealing with very large data col- lections, following a simple client-server approach is not going to work. Solution 1: For speeding up file accesses , apply striping techniques by which files can be fetched in parallel: File block of file a File block of file e e a c b d e a c b d b d e a c Whole-file distribution a a a b b b c c c e d d e e d File-striped system 11 – 4 Distributed File Systems/11.1 Architecture

Example: Google File System Solution 2: Divide files in large 64 MB chunks, and distribute/replicate chunks across many servers. file name, chunk index GFS client Master contact address Instructions Chunk-server state Chunk ID, range Chunk server Chunk server Chunk server Chunk data Linux file� Linux file� Linux file� system system system A couple of important details: • The master maintains only a (file name, chunk server) table in main memory ⇒ minimal I/O • Files are replicated using a primary-backup scheme; the master is kept out of the loop 11 – 5 Distributed File Systems/11.1 Architecture

RPCs in File Systems Observation: Many (traditional) distributed file sys- tems deploy remote procedure calls to access files. When wide-area networks need to be crossed, alter- natives need to be exploited: Client Server Client Server LOOKUP OPEN LOOKUP READ Lookup name Lookup name Open file READ Read file data Read file data Time Time (a) (b) 11 – 6 Distributed File Systems/11.3 Communication

Example: RPCs in Coda Observation: When dealing with replicated files, se- quentially sending information is not the way to go: Client Client Reply Invalidate Reply Invalidate Server Server Invalidate Reply Invalidate Reply Client Client Time Time (a) (b) Note: In Coda, clients can cache files, but will be in- formed when an update has been performed. 11 – 7 Distributed File Systems/11.3 Communication

File Sharing Semantics (1/2) Problem: When dealing with distributed file systems, we need to take into account the ordering of concur- rent read/write operations, and expected semantics (= consistency). Client machine #1 a b Process A a b c 1. Read "ab" 2. Write "c" File server Original file b a Single machine a b Process A 3. Read gets "ab" a b c Client machine #2 Process b a B Process B 1. Write "c" 2. Read gets "abc" (a) (b) 11 – 8 Distributed File Systems/11.5 Synchronization

File Sharing Semantics (2/2) UNIX semantics: a read operation returns the effect of the last write operation ⇒ can only be imple- mented for remote access models in which there is only a single copy of the file Transaction semantics: the file system supports trans- actions on a single file ⇒ issue is how to allow concurrent access to a physically distributed file Session semantics: the effects of read and write operations are seen only by the client that has opened (a local copy) of the file ⇒ what happens when a file is closed (only one client may actually win) 11 – 9 Distributed File Systems/11.5 Synchronization

Example: File Sharing in Coda Essence: Coda assumes transactional semantics, but without the full-fledged capabilities of real transactions. Session S A Client Open(RD) File f Invalidate Close Server Close Open(WR) File f Client Time Session S B Note: Transactional issues reappear in the form of “this ordering could have taken place.” 11 – 10 Distributed File Systems/11.5 Synchronization

Consistency and Replication Observation: In modern distributed file systems, client- side caching is the preferred technique for attaining performance; server-side replication is done for fault tolerance. Observation: Clients are allowed to keep (large parts of) a file, and will be notified when control is with- drawn ⇒ servers are now generally stateful 1. Client asks for file Client Server 2. Server delegates file Old file Local copy 3. Server recalls delegation Updated file 4. Client sends returns file 11 – 11 Distributed File Systems/11.6 Consistency and Replication

Example: Client-side Caching in Coda Session S Session S A A Client A Open(RD) Close Close Open(RD) Invalidate (callback break) File f File f Server File f OK (no file transfer) Open(WR) Open(WR) Close Close Client B Time Session S Session S B B Note: By making use of transactional semantics , it becomes possible to further improve performance. 11 – 12 Distributed File Systems/11.6 Consistency and Replication

Fault Tolerance Observation: FT is handled by simply replicating file servers, generally using a standard primary-backup protocol : Client Client Primary server for item x Backup server W1 W5 R1 R2 W4 W4 W3 W3 Data store W2 W3 W4 W1. Write request R1. Read request W2. Forward request to primary R2. Response to read W3. Tell backups to update W4. Acknowledge update W5. Acknowledge write completed 11 – 13 Distributed File Systems/11.7 Fault Tolerance

High Availability in P2P Systems Problem: There are many fully decentralized file-sharing systems, but because churn is high (i.e., nodes come and go all the time), we may face an availability prob- lem . Solution: Replicate files all over the place ( replica- tion factor : r rep ). Alternative: Apply erasure coding : • Partition a file F into m fragments, and recode into a collection F ∗ of n > m fragments • Property: any m fragments from F ∗ are sufficient to reconstruct F . • Replication factor: r ec = n / m 11 – 14 Distributed File Systems/11.7 Fault Tolerance

Replication vs. Erasure Coding With an average node availability a , and required file unavailability ǫ , we have for erasure coding : r ec · m � r ec · m � a i ( 1 − a ) r ec · m − i ∑ 1 − ǫ = i i = m and for file replication : 1 − ǫ = 1 − ( 1 − a ) r rep 2.2 r req r 2.0 ec 1.8 1.6 1.4 0.2 0.4 0.6 0.8 1 Node availability 11 – 15 Distributed File Systems/11.7 Fault Tolerance