Distributed Systems: Consistency Models

1
Distributed SystemsConsistency Models

• CS 654Lecture 17November 13, 2006

2

• Make up day on Tuesday?
• Second mid-term?

3
Consistency Models Review

• Enforcing absolute ordering is too expensive,
  especially with replication and caching.
• So we need to allow for mis-ordering.
• We could just do it casually. Tell programmers,
  Well, you always see things in exact order.
• They would say, What do you mean?
• So we need an exact, very precise way of
  specifying the kinds of inconsistencies that the
  application might see.
• That is the purpose and point of having
  consistency models.

4
Data Centric Consistency Models
5
Data Stores

• Consistency is viewed as read/write ops on shared
  data.
• A consistency model is a contract between the
  processes and the data store.

6
Notation

• Processes execute to the right as time
  progresses.
• The notation W1(x)a means that the process wrote
  the value a to the variable x.
• The notation R2(x)a means that the process read
  the value a from the variable x.
• The subscript is often dropped.

7
Sequential Consistency

• 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.
• There is some global order.
• Operations between processes must be as in the
  program.

Program A A-OP1A-OP2A-OP3
Global Order 2 A-OP1B-OP1A-OP2B-OP2B-OP3A-OP3
Global Order 3 A-OP1B-OP1A-OP2B-OP3B-OP2A-OP3
Global Order 1 A-OP1A-OP2A-OP3B-OP1B-OP2B-OP3
Program B B-OP1B-OP2B-OP3
8
Sequential Consistency (3)
9
Sequential Consistency (4)

• Figure 7-6. Three concurrently-executing
  processes.

10
Sequential Consistency (5)

• Figure 7-7. Four valid execution sequences for
  the processes of Fig. 7-6. The vertical axis is
  time.

11
Causal Consistency

• 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.

12
Causal Consistency (2)

• This sequence is allowed with a
  causally-consistent store, but not with a
  sequentially consistent store.

13
Causal Consistency

• Causally consistent?

14
Continuous Consistency (1)

• Figure 7-2. An example of keeping track of
  consistency deviations adapted from (Yu and
  Vahdat, 2002).

15
Continuous Consistency (2)

• Figure 7-3. Choosing the appropriate granularity
  for a conit. (a) Two updates lead to update
  propagation.

16
Continuous Consistency (3)

• Figure 7-3. Choosing the appropriate granularity
  for a conit. (b) No update propagation is needed
  (yet).

17
Grouping Operations

• Do SMP machines also need consistency models?
• Yes, there are many kinds.
• Why we not care about these when writing MT
  programs?
• We do, if we are platform dependent and dont use
  locks.
• How do we handle consistency in MT programs?
• Use locks.
• As viewed by an external, data-centric process,
  what do locks do?
• They turn non-atomic operations into atomic ones
  (functionally).
• In other words, they group them.

18
Synchronization Variables

• Operations are grouped via synchronization
  variables (locks).
• Each sync var protects an associated data.
• Each kind of sync var has some associated
  properties.

19
Grouping Operations

• Entry Consistency Necessary criteria for correct
  synchronization
• An acquire access of a synchronization variable
  is not allowed to perform until all updates to
  guarded shared data have been performed with
  respect to that process.
• Before exclusive mode access to synchronization
  variable by a process is allowed to perform with
  respect to that process, no other process may
  hold the synchronization variable, not even in
  nonexclusive mode.
• After exclusive mode access to a 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
  variables owner.

20
Grouping Operations (2)

• A valid event sequence for entry consistency.

21
Client Centric Models
22
Weaker Models

• Sometimes strong models are needed, if the result
  of race conditions are very bad.
• Banks
• Sometimes the result of races are just
  inefficiency, or inconvenience, etc.
• How strong is Orbitzs model?
• If it shows a ticket available, is it really?
• How does it prevent two people from reserving the
  same seat?
• One kind of weaker model is eventual consistency
• It eventually becomes consistent

23
Eventual Consistency

• How well does EC work for mobile clients?
• Client-centric is for this. Consistent for a
  single client.

24
Notation

• xit is the version of x at local copy Li at
  time t.
• Version xit is the result of a series of write
  operations at Li that took place since
  initialization. This is WS(xit).
• If operations in WS(xit) have also been
  performed at local copy Lj at a later time t2, we
  write WS(xit1xjt2).

25
Monotonic Reads

• 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.

26
Monotonic Reads
27
Monotonic Writes

• 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.

28
Monotonic Writes
29
Read Your Writes

• 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.
• Suppose your web browser has a cache.
• You update your web page on the server.
• You refresh your browser.
• Do you have read-your-writes consistency?

30
Read Your Writes (2)
31
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.

32
Writes Follow Reads (2)

• Figure 7-15. (a) A writes-follow-reads consistent
  data store.

33
Writes Follow Reads (3)

• Figure 7-15. (b) A data store that does not
  provide writes-follow-reads consistency.