Transcript Introduction - unisannio.it

DISTRIBUTED SYSTEMS
Principles and Paradigms
Second Edition
ANDREW S. TANENBAUM
MAARTEN VAN STEEN
Chapter 7
Consistency And Replication
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Reasons for Replication
•
Data are replicated to increase the reliability of a
system.
•
Replication for performance

Scaling in numbers

Scaling in geographical area

Caveat

Gain in performance

Cost of increased bandwidth for maintaining
replication
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Replication
Synchronous replication
•
A read operation returns the same results on every
copy;
•
Write operations are atomic: they are propagated on all
nodes before other operations can happen.
To improve synchronization the consistency constraints can
be “relaxed”.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Data-centric Consistency Models
Figure 7-1. The general organization of a logical
data store, physically distributed and
replicated across multiple processes.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Consistency model
Consistency model
•
A “Contract” between processes and data store
Continuous consistency measure deviations in replicas:
•
numerical value: absolute/relative;
•
staleness: last time the replica was updated;
•
ordering of update operations.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Consistency unit
Conit: measure of inconsistency.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Continuous Consistency (1)
Figure 7-2. An example of keeping track of consistency
deviations [adapted from (Yu and Vahdat, 2002)].
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Continuous Consistency (2)
Figure 7-3. Choosing the appropriate granularity for a conit.
(a) Two updates lead to update propagation.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Continuous Consistency (3)
Figure 7-3. Choosing the appropriate granularity for a conit.
(b) No update propagation is needed (yet).
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Sequential Consistency (1)
Figure 7-4. Behavior of two processes operating
on the same data item. The horizontal axis is time.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Sequential Consistency (2)
A data store is sequentially consistent when:
The result of any execution is the same as if the (read and
write) operations by all processes on the data store …
•
•
were executed in some sequential order and …
the operations of each individual process appear …

in this sequence

in the order specified by its program.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Sequential Consistency (3)
Figure 7-5. (a) A sequentially consistent data store.
(b) A data store that is not sequentially consistent.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Sequential Consistency (4)
Figure 7-6. Three concurrently-executing processes.
6!=720 possible execution sequences
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Sequential Consistency (5)
Figure 7-7. Four valid execution sequences for the processes of
Fig. 7-6. The vertical axis is time.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Sequential Consistency (6)
Examples of not valid sequences
•
000000:
–
•
statements must be executed in program order;
001001:
–
–
–
00: y=z=0  both statements of P1 executed;
10: P2 must run after P1 starts;
01: P3 complete before P1 starts.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Causal Consistency (1)
For a data store to be considered causally
consistent, it is necessary that the store obeys
the following condition:
Writes that are potentially causally related …
• must be seen by all processes
• in the same order.
Concurrent writes …
• may be seen in a different order
• on different machines.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Causal Consistency (2)
Figure 7-8. This sequence is allowed with a causally-consistent
store, but not with a sequentially consistent store.
W1(x)c and W2(x)b are concurrent
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Causal Consistency (3)
Figure 7-9. (a) A violation of a causally-consistent store.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Causal Consistency (4)
Figure 7-9. (b) A correct sequence of events in a causallyconsistent store.
Note: this is not acceptable in sequentially consistency
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Critical sections
Mutual exclusion, transactions
Use synchronization variables:
•
Acquire when enter in section
•
Release when leave the section
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Solution to Critical-Section Problem
1. Mutual Exclusion - If process Pi is executing in its critical section, then
no other processes can be executing in their critical sections
2. Progress - If no process is executing in its critical section and there
exist some processes that wish to enter their critical section, then the
selection of the processes that will enter the critical section next
cannot be postponed indefinitely
3. Bounded Waiting - A bound must exist on the number of times that
other processes are allowed to enter their critical sections after a
process has made a request to enter its critical section and before that
request is granted
 Assume that each process executes at a nonzero speed
 No assumption concerning relative speed of the N processes
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Grouping Operations (1)
Necessary criteria for correct synchronization:
•
An acquire access of a synchronization variable, not
allowed to perform until all updates to guarded shared
data have been performed with respect to that process.
The acquire may not complete until the guarded shared data
are up to date. All remote changes must be made visible.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Grouping Operations (2)
•
Before exclusive mode access to synchronization
variable by process is allowed to perform with respect to
that process, no other process may hold synchronization
variable, not even in nonexclusive mode.
Before update shared data a process must enter in critical
sections in exclusive mode to be sure no other processes
can update the data at the same time.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Grouping Operations (3)
•
After exclusive mode access to synchronization variable
has been performed, any other process’ next
nonexclusive mode access to that synchronization
variable may not be performed until it has performed with
respect to that variable’s owner.
If a process wants to enter in a critical region in nonexclusive
mode, it must first check with the owner of
synchronization variables to fetch the most recent copies
of shared data.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Grouping Operations (4)
Figure 7-10. A valid event sequence for entry consistency.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Consistency and Coherence
Consistency model
•
what can be expected when a set of concurrent
processes operate on data. (Read and write
operations on a set of data items)
Coherence model
•
what can be expected on a single data item.
(sequential consistency on a single data item).
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Eventual consistency
Most application can tolerate a high degree of inconsistency.
If no update take place for a long time all replicas will
gradually become consistent.
Es. DNS, web-pages
•
•
updates are guaranteed to propagate to all replicas;
write-write conflicts are solved assuming that only few
processes can update data
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Client-Centric Consistency
Figure 7-11. The principle of a mobile user accessing
different replicas of a distributed database.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Client centric consistency
Consistency for a single client
NO guarantee of consistency for concurrent accesses of
different client
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Monotonic Reads (1)
A data store is said to provide monotonic-read
consistency if the following condition holds:
If a process reads the value of a data item x …
• any successive read operation on x by that
process
• will always return that same value
• or a more recent value.
Ex. distributed email database
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Monotonic Reads (2)
Insieme delle
operazioni su x1 e x2
Figure 7-12. The read operations performed by a single process P
at two different local copies of the same data store.
(a) A monotonic-read consistent data store.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Monotonic Reads (3)
Figure 7-12. The read operations performed by a single process P
at two different local copies of the same data store.
(b) A data store that does not provide monotonic reads.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Monotonic Writes (1)
In a monotonic-write consistent store, the following
condition holds:
A write operation by a process on a data item x …
• is completed before any successive write
operation on x
• by the same process.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Monotonic Writes (1)
A write operation by a process on a copy of item x
is performed only if that copy is update by
means of any preceding write operation on
other copies of x
FIFO consistency.
Ex. Update systems: to apply an update all previous
updates have to be applied.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Monotonic Writes (2)
Figure 7-13. The write operations performed by a single process P
at two different local copies of the same data store. (a) A
monotonic-write consistent data store.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Monotonic Writes (3)
Figure 7-13. The write operations performed by a single process P
at two different local copies of the same data store. (b) A data
store that does not provide monotonic-write consistency.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Read Your Writes (1)
A data store is said to provide read-your-writes
consistency, if the following condition holds:
The effect of a write operation by a process on data
item x …
• will always be seen by a successive read
operation on x
• by the same process.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Read Your Writes (2)
Figure 7-14. (a) A data store that provides
read-your-writes consistency.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Read Your Writes (3)
Figure 7-14. (b) A data store that does not.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Writes Follow Reads (1)
A data store is said to provide writes-follow-reads
consistency, if the following holds:
A write operation by a process …
• on a data item x following a previous read
operation on x by the same process …
• is guaranteed to take place on the same or a
more recent value of x that was read.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Writes Follow Reads (2)
Figure 7-15. (a) A writes-follow-reads consistent data store.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Writes Follow Reads (3)
Figure 7-15. (b) A data store that does
not provide writes-follow-reads consistency.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Replica-Server Placement
Replica servers
•
scegliere il miglior posto per posizionare un server
Content
•
Scegliere il miglior server per inserire i contenuti
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Replica-Server Placement
Figure 7-16. Choosing a proper cell size for server placement.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Content Replication and Placement
Figure 7-17. The logical organization of different kinds
of copies of a data store into three concentric rings.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Content Replication and Placement
Es. siti web:
•
Cluster web server
•
Mirroring
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Server-Initiated Replicas
Figure 7-18. Counting access requests from different clients.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Server-Initiated Replicas
•
•
Conteggio degli accessi per capire quando replicare
o cancellare le repliche;
Meccanismi per garantire che almeno una copia
sopravviva.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
State versus Operations
Possibilities for what is to be propagated:
1. Propagate only a notification of an update
(invalidation protocols).
2. Transfer data from one copy to another.
3. Propagate the update operation to other
copies (active replication).
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Pull versus Push Protocols
Push
•
Protocolli Server-based
•
Le modifiche sono propagate senza che le repliche lo
richiedano
•
Alto grado di consistenza
Pull
•
Protocolli client-based
•
Le modifiche sono propagate solo quando richieste da
chi le deve usare
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Pull versus Push Protocols
Figure 7-19. A comparison between push-based and pull-based
protocols in the case of multiple-client, single-server systems.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Lease
Push temporaneo:
•
Un client richiede update automatici per un tempo
limitato
•
Scaduto il periodo si usano protocolli pull, o si richiede
un nuovo lease
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Protocolli di consistenza
Implementazione di un modello di consistenza.
Primary-based protocols
•
Usati per la consistenza sequenziale
•
Ogni item ha una replica primaria
•
Tutte le scritture sono applicate e coordinate dalla
copia primaria
Replication-based
•
Le operazioni possono essere eseguite su una replica
qualsiasi.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Remote-Write Protocols
Figure 7-20. The principle of a primary-backup protocol.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Local-Write Protocols
Figure 7-21. Primary-backup protocol in which the primary
migrates to the process wanting to perform an update.
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Replicated-write protocols
•
•
•
No single primary copy
Writes can be performed at multiple replicas
Two types:
–
–
Active Replication: All operations are forwarded to all replicas
Quorum-based: Operations are forwarded to a subset of all
replicas
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Active replication
•
•
•
All operations are propagated to all replicas
More complex to achieve consistency
Need to order operations
–
–
Use Lamport timestamps
Central sequencer
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Quorum-Based Protocols
•
•
•
Operations are sent to a subset of replicas
Maintaining consistency
Use voting
–
–
–
If a quorum (e.g.: majority) agrees, then, consistency is maintained
Write: Apply write only if majority of replicas agree on the update
Read: Perform read only if majority of replicas agree on the
current data value
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5
Quorum-Based Protocols
Figure 7-22. Three examples of the voting algorithm. (a) A
correct choice of read and write set. (b) A choice that
may lead to write-write conflicts. (c) A correct choice,
known as ROWA (read one, write all).
Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5