1. Linearizability Linearizability is a correctness criterion for concurrent object (Herlihy & Wing ACM TOPLAS 1990). It provides the illusion that each operation on the object takes effect in zero time, and the results are “equivalent to” some legal sequential computation.

2. Linearizability A trace is in a read-write system is consistent, when every read returns the latest value written into the shared variable preceding that read operation. A trace is linearizable, when (1) it is consistent, and (2) the temporal ordering among the reads and writes is respected. W (x:=0)R (x=1) W (x:=1) R(x=1) (Initially x=y=0) W (x:=0) Is it a linearizable trace?

3. Sequential consistency Some interleaving of the local temporal order of events at the different replicas is a consistent trace. W(x:=100) W(x:=99] R(x=100) R(x=99)

4. Sequential consistency Is sequential consistency satisfied here? Assume that initially x=y=0. W(x:=10) W(x:=8] W(x=20) R(x=20) R(x:=10) R(x=10)

5. Causal consistency All writes that are causally related must be seen by every process in the same order. W(x:=10) W(x:=20) R(x=10)R(x=20) R(x=10)

6. Implementing consistency models Why are there so many consistency models? Each model has a use in some application. The cost of implementation (as measured by message complexity) decreases as the models become “weaker”.

7. Implementing linearizability Read x W(x:=10) W (x:=20) Needs total order multicast of all reads and writes Read x

8. Implementing linearizability The total order broadcast forces every process to accept and handle all reads and writes in the same temporal order. The peers update their copies in response to a write, but only send acknowledgements for reads. After this, the local copy is returned

9. Implementing sequential consistency Use total order broadcast all writes only, but for reads, immediately return local copies.

10. Exercise Let x, y be two shared variables Process PProcess Q { initially x=0}{ initially y=0} x :=1;y:=1; if y=0  x:=2 fi ; if x=0  y:=2 fi ; Print xPrint y If sequential consistency is preserved, then what are the possible values of the printouts? List all of them.

11. Client centric consistency model

12. Read-after-read If read from A is followed by read from B then the second read should return a data that is as least as new as the previous read. This means, once read, subsequent reads by the client must return the same value, or a more recent value A B Iowa City San Francisco Read x = 20 (ts = 42) Read x = 30 (ts = 60) Or read x = 75 (ts = 90)

13. Client centric consistency model Write-after-write A write by a client must be propagated to all replicas before a subsequent write by the same client takes effect regardless of the location of replicas Iowa City New York Alabama Florida W(x:=10)W(x:=20)

14. Client centric consistency model Read-after-write Each process must be able to see its own writes. Consider updating a webpage. If the editor and the browser are not integrated, the editor will send the updated HTML page to the server, but the browser in a different location may return an old copy of the page when you view it. This is bad. Edit webpage here Server Browse the webpage Server

15. Client centric consistency model Write-after-read Each write operation following a read should take effect on the previously read copy, or a more recent version of it. x:=0 x:=20 x:= x+5 x=5 or 25? Write should take effect on x=20 Iowa city San Francisco Alice then went to San Francisco

16. Quorum-based protocols Replicas of databases improve the availability. To synchronize the updates, a quorum system engages a designated minimum number of the replicas for every read or write operation – this number is called the read or write quorum. When the quorum is not met, the operation (read or write) is not performed.

17. The architecture RM replicas Replica managers clients

18. Quorum-based protocols To write, update > N/2 of them, and tag it with new version number. To read, access > N/2 replicas with version numbers. Otherwise abandon the read. Thomas rule N = no of replicas. Write quorum Ver 3 Ver 2 Read quorum

19. Rationale N = no of replicas. Ver 3 Ver 2 If different replicas store different version numbers for an item, the state associated with a larger version number is more recent than the state associated with a smaller version number. We require that R+W > N, i.e., read quorums always intersect with write quorums. This will ensure that read results always reflect the result of the most recent write (because the read quorum will include at least one replica that was involved in the most recent write).

20. How it works 1. Send a write request containing the state and new version number to all the replicas and waits to receive acknowledgements from a write quorum. At that point the write operation is complete. 2. Send a read request for the version number to all the replicas, and wait for replies from a read quorum. N = no of replicas.

21. Quorum-based protocols After a partition, only the larger segment runs the consensus protocol. The smaller segment contains stale data, until the network is repaired. Ver.0 Ver.1

22. Quorum-based protocols No partition satisfies the read or write quorum

23. Quorum-based protocols Asymmetric quorum: W + R > N W > N/2 No two writes overlap No read overlaps with a write. R = read quorumW = write quorum