Consistency and Replication

1. Consistency and Replication Chapter 6

2. Release Consistency (1) • A valid event sequence for release consistency. • Use acquire/release operations to denote critical regions. • Can also use a barrier synchronization.

3. Release Consistency (2) • Rules: • Before a read or write operation on shared data is performed, all previous acquires done by the process must have completed successfully. • Before a release is allowed to be performed, all previous reads and writes by the process must have completed • Accesses to synchronization variables are FIFO consistent (sequential consistency is not required).

4. Release Consistency (3) • Lazy release consistency – wait until the next process needs the data to send the update • Eager release consistency – send the update out as soon as it is released by the process using it.

5. Entry Consistency • Associate each data variable with a specific synchronization or lock variable

6. Entry Consistency (1) • Conditions: • An acquire access of a synchronization variable is not allowed to perform with respect to a process until all updates to the guarded shared data have been performed with respect to that process. • Before an exclusive mode access to a 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 an exclusive mode access to a synchronization variable has been performed, any other process's next nonexclusive mode access to that synchronization variable may not be performed until it has performed with respect to that variable's owner.

7. Entry Consistency (1) • A valid event sequence for entry consistency.

8. Summary of Consistency Models • Consistency models not using synchronization operations. • Models with synchronization operations.

9. Eventual Consistency • The principle of a mobile user accessing different replicas of a distributed database.

10. Monotonic Reads • 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.

11. Monotonic Reads • The read operations performed by a single process P at two different local copies of the same data store. • A monotonic-read consistent data store • A data store that does not provide monotonic reads.

12. Monotonic Writes • A write operation by a process on a data item x is completed before any successive write operation on x by the same process.

13. Monotonic Writes • The write operations performed by a single process P at two different local copies of the same data store • A monotonic-write consistent data store. • A data store that does not provide monotonic-write consistency.

14. Read Your Writes • 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.

15. Read Your Writes • A data store that provides read-your-writes consistency. • A data store that does not.

16. Writes Follow Reads • 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.

17. Writes Follow Reads • A writes-follow-reads consistent data store • A data store that does not provide writes-follow-reads consistency

18. Replica Placement • The logical organization of different kinds of copies of a data store into three concentric rings.

19. Server-Initiated Replicas • Counting access requests from different clients.

20. Client-Initiated Replicas • Really the same as client-side caches • It is also possible to have clients retrieve data from other client caches • Cache Only Memory Architecture • Used in the KSR supercomputer to construct distributed shared memory • Can also dynamically change the size of the cache according to the demand placed on it • Cooperative cache

21. Update Propagation • Three possibilities: • Propagate only a notification of an update • Transfer data from one copy to another • Propagate the update operation to other copies

22. Pull versus Push Protocols • Push-based approach • Also called a server-based protocol • Server initiates the transfer without the client asking for it • Pull-based approach • Transfer happens when the client asks for it • Advantages depend on the type of workload • Amount of data, frequency of update, frequency of read-only operations

23. Pull versus Push Protocols • A comparison between push-based and pull-based protocols in the case of multiple client, single server systems.

24. Lease Protocols • Have the copy expire after a period of time • A client can ask to renew a lease • A short lease can be given for a client than only uses the item infrequently – the server doesn’t have to maintain state for as long

25. Remote-Write Protocols (1) • Primary-based remote-write protocol with a fixed server to which all read and write operations are forwarded.

26. Remote-Write Protocols (2) • The principle of primary-backup protocol.

27. Local-Write Protocols (1) • Primary-based local-write protocol in which a single copy is migrated between processes.

28. Local-Write Protocols (2) • Primary-backup protocol in which the primary migrates to the process wanting to perform an update.

29. Active Replication (1) • The problem of replicated invocations.

30. Active Replication (2) • Forwarding an invocation request from a replicated object. • Returning a reply to a replicated object.

31. Quorum-Based Protocols • Three examples of the voting algorithm: • A correct choice of read and write set • A choice that may lead to write-write conflicts • A correct choice, known as ROWA (read one, write all)

32. Orca OBJECT IMPLEMENTATION stack; top: integer; # variable indicating the top stack: ARRAY[integer 0..N-1] OF integer # storage for the stack OPERATION push (item: integer) # function returning nothing BEGIN GUARD top < N DO stack [top] := item; # push item onto the stack top := top + 1; # increment the stack pointer OD; END; OPERATION pop():integer; # function returning an integer BEGIN GUARD top > 0 DO # suspend if the stack is empty top := top – 1; # decrement the stack pointer RETURN stack [top]; # return the top item OD; END;BEGIN top := 0; # initializationEND; • A simplified stack object in Orca, with internal data and two operations.

33. Management of Shared Objects in Orca • Four cases of a process P performing an operation on an object O in Orca.

34. Causally-Consistent Lazy Replication • The general organization of a distributed data store. Clients are assumed to also handle consistency-related communication.

35. Processing Read Operations • Performing a read operation at a local copy.

36. Processing Write Operations • Performing a write operation at a local copy.