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Consistency and Replication

Consistency and Replication. Chapter 6. Replication. Important issue in DSs to enhance reliability and improve performance. The major problem is how to make replicas consistent. Much attention on consistency have been paid on consistency models of DSM systems by parallel computer designers.

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Consistency and Replication

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  1. Consistency and Replication Chapter 6 OS2 –Sem 1, 86-87 Rasool Jalili

  2. Replication • Important issue in DSs to enhance reliability and improve performance. • The major problem is how to make replicas consistent. • Much attention on consistency have been paid on consistency models of DSM systems by parallel computer designers. • Issue: • Client-Centric consistency • Implementation of replication and distribution of updates. • Models of consistency: Strong? How much? OS2 –Sem 1, 86-87 Rasool Jalili

  3. Object Replication (1) • Organization of a distributed remote object shared by two different clients. OS2 –Sem 1, 86-87 Rasool Jalili

  4. The first problem is how to handle concurrent accesses to the remote object by two clients. • 1- The object itself handles concurrent invocations • 2- The server is responsible for concurrency control and the object is unprotected. • The two scenarios in the next slide. OS2 –Sem 1, 86-87 Rasool Jalili

  5. Object Replication (2) • A remote object capable of handling concurrent invocations on its own. • A remote object for which an object adapter is required to handle concurrent invocations OS2 –Sem 1, 86-87 Rasool Jalili

  6. Object Replication (3) • A distributed system for replication-aware distributed objects. • A distributed system responsible for replica management OS2 –Sem 1, 86-87 Rasool Jalili

  7. Replication • Replication and Scaling Technology? • Consistency is against scalability! • Consistency Model is a contract between processes and the data store. OS2 –Sem 1, 86-87 Rasool Jalili

  8. Data-Centric Consistency Models • The general organization of a logical data store, physically distributed and replicated across multiple processes. OS2 –Sem 1, 86-87 Rasool Jalili

  9. Strict Consistency Any read on a data item X returns a value corresponding to the result of the most recent write on X • Behavior of two processes, operating on the same data item. • (a) A strictly consistent store. • (b) A store that is not strictly consistent. OS2 –Sem 1, 86-87 Rasool Jalili

  10. Linearizability and Sequential Consistency (1) • A sequentially consistent data store. • A data store that is not sequentially consistent. OS2 –Sem 1, 86-87 Rasool Jalili

  11. Linearizability and Sequential Consistency (2) • Three concurrently executing processes. OS2 –Sem 1, 86-87 Rasool Jalili

  12. Linearizability and Sequential Consistency (3) • Four valid execution sequences for the processes of the previous slide. The vertical axis is time. 000000 or 001001 are impossible. If a DSM do not produce some valid patterns!! OS2 –Sem 1, 86-87 Rasool Jalili

  13. Casual Consistency (1) • Necessary condition:Writes that are potentially casually related must be seen by all processes in the same order. Concurrent writes may be seen in a different order on different machines. OS2 –Sem 1, 86-87 Rasool Jalili

  14. Casual Consistency (2) • This sequence is allowed with a casually-consistent store, but not with sequentially or strictly consistent store. OS2 –Sem 1, 86-87 Rasool Jalili

  15. Casual Consistency (3) • A violation of a casually-consistent store. • A correct sequence of events in a casually-consistent store. OS2 –Sem 1, 86-87 Rasool Jalili

  16. FIFO Consistency (1) • Necessary Condition:Writes done by a single process are seen by all other processes in the order in which they were issued, but writes from different processes may be seen in a different order by different processes. OS2 –Sem 1, 86-87 Rasool Jalili

  17. FIFO Consistency (2) • A valid sequence of events of FIFO consistency OS2 –Sem 1, 86-87 Rasool Jalili

  18. FIFO Consistency (3) • Statement execution as seen by the three processes from the previous slide. The statements in bold are the ones that generate the output shown. OS2 –Sem 1, 86-87 Rasool Jalili

  19. FIFO Consistency (4) • Two concurrent processes. • Compare Sequential and FIFO consistency. OS2 –Sem 1, 86-87 Rasool Jalili

  20. Weak Consistency (1) Properties: • Accesses to synchronization variables associated with a data store are sequentially consistent • No operation on a synchronization variable is allowed to be performed until all previous writes have been completed everywhere • No read or write operation on data items are allowed to be performed until all previous operations to synchronization variables have been performed. OS2 –Sem 1, 86-87 Rasool Jalili

  21. Weak Consistency (2) • A program fragment in which some variables may be kept in registers; a and b in registers, but when calling f, they should be synchronized. int a, b, c, d, e, x, y; /* variables */int *p, *q; /* pointers */int f( int *p, int *q); /* function prototype */ a = x * x; /* a stored in register */b = y * y; /* b as well */c = a*a*a + b*b + a * b; /* used later */d = a * a * c; /* used later */p = &a; /* p gets address of a */q = &b /* q gets address of b */e = f(p, q) /* function call */ OS2 –Sem 1, 86-87 Rasool Jalili

  22. Weak Consistency (3) • A valid sequence of events for weak consistency. • An invalid sequence for weak consistency. OS2 –Sem 1, 86-87 Rasool Jalili

  23. Release Consistency • Problem with Weak Consistency: Access to a synch variable might be because of finishing or starting of write operation on shared data items.  in all cases, it should propagates all recent writes around the system. • A more efficient way is to separate entering and finishing of a critical region. • Acquire and Release. • The programmer is responsible to put them in its program • Similar to lock and unlock. OS2 –Sem 1, 86-87 Rasool Jalili

  24. Release Consistency (1) • A valid event sequence for release consistency. OS2 –Sem 1, 86-87 Rasool Jalili

  25. 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). OS2 –Sem 1, 86-87 Rasool Jalili

  26. Lazy or Eager • Eager release consistency pushes the update on Release • Lazy release consistency does not do anything on release. The process doing Acquire after that, updates its copies. OS2 –Sem 1, 86-87 Rasool Jalili

  27. Entry Consistency (1) • A valid event sequence for entry consistency. OS2 –Sem 1, 86-87 Rasool Jalili

  28. Entry Consistency (2) 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. OS2 –Sem 1, 86-87 Rasool Jalili

  29. Summary of Consistency Models • Consistency models not using synchronization operations. • Models with synchronization operations. OS2 –Sem 1, 86-87 Rasool Jalili

  30. Client-Centric Consistency • Prev. discussion was concentrated on data items. • Assuming read operations are much more than concurrent writes:: so we need even weaker consistency • The level of concurrency and the level of requirement of being so consistent varies. The question is that: How fast updates should be made available to only-reading processes? • Example: DNS, Web pages? These are able to tolerate a high degree of inconsistency. • The base idea: If no updates take place for a long time, all replicas will gradually become consistent  Eventual consistency. • Generally a small set of replicas do write and so write-write conflicts can be handled. OS2 –Sem 1, 86-87 Rasool Jalili

  31. Eventual Consistency The problem of having mobile clients moving around and connect to different replicas!  Client-Centric Consistency. Guarantees consistency for a single client OS2 –Sem 1, 86-87 Rasool Jalili

  32. Client-Centric Consistency • Assume each client access its nearest replica and does its read and write • Updates are eventually propagated to the other copies. • We can simplify the issue by considering an owner for each data item who has the authority on the data item. • xi [t] means the version of x at Li at time t. • WS(xi [t]) denotes the set of writes on x. • 4 models of Client-Centric consistency as follows … OS2 –Sem 1, 86-87 Rasool Jalili

  33. Monotonic Reads (1) A data store is said to provide Monotonic Read consistency if: • When a process reads a data item x, successive read operations of the same process on x always returns the same value or a more recent value. • Example: Our mailbox, even when it is stored in a replicated and fully distributed around the world. OS2 –Sem 1, 86-87 Rasool Jalili

  34. 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. OS2 –Sem 1, 86-87 Rasool Jalili

  35. Monotonic Writes(1) • A write operation by a process on a data item x is completed before any successive write on x by the same process, no matter where the operation was initiated. • Similar to the concept of FIFO consistency on data-centric approaches, but here we are restricted to a single process, not all concurrent processes. • Ex: software library update OS2 –Sem 1, 86-87 Rasool Jalili

  36. 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. OS2 –Sem 1, 86-87 Rasool Jalili

  37. Read Your Writes • The effect of a write operation by a process on data item x will always be seen by a successive read op on x by the same process. • Ex: Updating web page and seeing that ! OS2 –Sem 1, 86-87 Rasool Jalili

  38. Read Your Writes • A data store that provides read-your-writes consistency. • A data store that does not. OS2 –Sem 1, 86-87 Rasool Jalili

  39. Writes Follow Reads • A write operation by a process on a data item x following a previous read on x by the same process, is guaranteed to take place on the same or a more recent value of x that was read. • Ex: Posting of a reaction on an article in newsgroups after seeing the original article. OS2 –Sem 1, 86-87 Rasool Jalili

  40. Writes Follow Reads • A writes-follow-reads consistent data store • A data store that does not provide writes-follow-reads consistency OS2 –Sem 1, 86-87 Rasool Jalili

  41. Replicas • Permanent replicas; number of them is limited. Mirroring is a form of permanent replicas • Server initiated replicas • Client-initiated replicas. OS2 –Sem 1, 86-87 Rasool Jalili

  42. Replica Placement • The logical organization of different kinds of copies of a data store into three concentric rings. OS2 –Sem 1, 86-87 Rasool Jalili

  43. Server initiated replicas • Copies of data store exist to enhance performance; created at he initiative of the owner of the data store due to a burst traffic :: Push cache. • Where and when replicas should be created and deleted? • An approach to dynamically replicate files , designed for web-hosting :: updates are rare compare to read ops. OS2 –Sem 1, 86-87 Rasool Jalili

  44. Two issues: • Replication for reduction of load on a server • Migration or replication of specific files to the server closer to the clients who issue many requests to those files. • Each server keeps track of access counts per file and the location of the access. Each server can determine which server is closest to a given client C. • When the number of req for a file at server S drops below a deletion threshold, it can be removed from S. • Two thresholds: del (S, F), and rep (S, F) OS2 –Sem 1, 86-87 Rasool Jalili

  45. Server-Initiated Replicas • Counting access requests from different clients. OS2 –Sem 1, 86-87 Rasool Jalili

  46. Client-initiated replicas • Created at the initiative of clients • Known as (client) caches. • Cache hit • Period of keeping data on caches OS2 –Sem 1, 86-87 Rasool Jalili

  47. Update Propagation • Update normally initiated at the client side and subsequently forwarded to one of the copies.  update should be propagated toward other copies whilst maintaining consistency. • Issues: • What should be propagated: State or operations? • Only a notification of an update :: invalidation • Transfer data from one copy to another. • Propagate the update op to other copies. • Push updates or pull them? OS2 –Sem 1, 86-87 Rasool Jalili

  48. Pull versus Push Protocols A comparison between push-based and pull-based protocols in the case of multiple client (each having their own cache), single server systems. OS2 –Sem 1, 86-87 Rasool Jalili

  49. Consistency Protocols • So far, we concentrated on consistency models. • A consistency protocol describes an implementation of a specific consistency model. • Primary-Based protocols • Client-server: all RW ops from a server (distributed or centralized) • Primary-Backup protocols (Write operations toward the primary) OS2 –Sem 1, 86-87 Rasool Jalili

  50. Remote-Write Protocols (1) Primary-based remote-write protocol with a fixed server to which all read and write operations are forwarded. OS2 –Sem 1, 86-87 Rasool Jalili

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