1. Consistency And Replication
Ömer Faruk SARAÇ

2. Outline Introduction Data-Centric Consistency Models
Reasons for Replication
Replication as Scaling Technique
Data-Centric Consistency Models
Continuous Consistency
Consistent Ordering of Operations
Client-Centric Consistency Models
Eventual Consistency
Monotonic Reads
Monotonic Writes
Read Your Writes
Writes Follow Reads
Replica Management
Replica Server Placement
Content Replication and Placement
Content Distribution
Consistency Protocols

3. Reasons for Replication
Enhance Reliability
Improve Performance
Scaling in numbers
Scaling in geographical area
Caching

4. Replication as Scaling Technique
Placing replicas(data) close to clients
Network bandwidth issue
Cache
How to keep replicas consistent?
Loosen constraints

5. Data-Centric Consistency Models
Introduction
Reasons for Replication
Replication as Scaling Technique
Data-Centric Consistency Models
Continuous Consistency
Consistent Ordering of Operations
Client-Centric Consistency Models
Eventual Consistency
Monotonic Reads
Monotonic Writes
Read Your Writes
Writes Follow Reads
Replica Management
Replica Server Placement
Content Replication and Placement
Content Distribution
Consistency Protocols

6. Data-Centric Consistency
Data store
Shared data
Shared memory
Shared database
Distributed file system
Contracts between processes(clients) and data store(replicas)

7. Continuous Consistency
Inconsistencies
Deviation in numerical values
Deviation in staleness
Deviation with respect to ordering of updates
Conit
Consistency unit
Vector clock representation
Granularity of conit
Too small, hard to manage systemware
Too big, irrelevant data packages
Libraries for applications

8. Continuous Consistency

9. Consistent Ordering of Operations
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.
Valid execution sequences
Output signature

10. Consistent Ordering of Operations
Sequential Consistency

11. Consistent Ordering of Operations
Casual Consistency
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.
Dependency graph
Weaker than squential consistency

12. Consistent Ordering of Operations
Casual Consistency

13. Consistent Ordering of Operations
Grouping Operations
Hardware based
Shared memory multiprocessor systems
Synchonization parameters
Critical section
Acquire-release sync variable
Entry consistency
Associate lock with each data item

14. Consistent Ordering of Operations
Grouping Operations

15. Data-Centric Consistency Models
Introduction
Reasons for Replication
Replication as Scaling Technique
Data-Centric Consistency Models
Continuous Consistency
Consistent Ordering of Operations
Client-Centric Consistency Models
Eventual Consistency
Monotonic Reads
Monotonic Writes
Read Your Writes
Writes Follow Reads
Replica Management
Replica Server Placement
Content Replication and Placement
Content Distribution
Consistency Protocols

16. Client-Centric Consistency
Special class of distrubuted systems
Lack of simultaneous updates or easily resolved
Weak consistency models
Many consistencies are hidden relatively cheap way

17. Eventual Consistency Few processes perform operates
Mostly read data from data store
Examples
DNS
Web Cache servers
Eventually all replicas will be consistent
Mobile clients issue

18. Eventual Consistency

19. Monotonic Reads
If a process reads the value of a data item x, then any successive read operation on x by that process will always return that same value or a more recent value.
But no guarantees on concurrent access by different clients.
Mailbox example

20. Monotonic Reads

21. 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.
Data centric FIFO consistency
Correct order of write operations
Software library example

22. Monotonic Writes

23. 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.
Examples
Web page caches
Password management

24. Read Your Writes

25. Writes Follows 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.
Network newsgroup example

26. Writes Follows Reads

27. Data-Centric Consistency Models
Introduction
Reasons for Replication
Replication as Scaling Technique
Data-Centric Consistency Models
Continuous Consistency
Consistent Ordering of Operations
Client-Centric Consistency Models
Eventual Consistency
Monotonic Reads
Monotonic Writes
Read Your Writes
Writes Follow Reads
Replica Management
Replica Server Placement
Content Replication and Placement
Content Distribution
Consistency Protocols

28. Replica Management How, where, when
Placement, activation, deployment, migration
Server placement
Best place to locate a server(data store)
Content placement
Best replica to copy data sote

29. Replica Server Placement
Simple issue: money
Choose best places K in set of possible places N where K<N
Network latency
Bandwidth
Physical distance
İgnore client location, use network topology
Defining regions
Dence cells
Cell, two dimensional rectangle
Too small, many replicas in a cell
Too large, too few clusters for a cell

30. Replica Server Placement

31. Content Replication and Placement

32. Content Replication and Placement
Permanent replicas
Generally in small numbers
Initial set of replicas
Distribution of web site example
Mirror servers
Forward to one of the server, round-robin
Distributed database servers, different machines

33. Content Replication and Placement
Server-initiated Replicas
Dynamic replication
Monitor incoming requests
Install a number of temporary replicas
Web hosting services example
Where to put which content
Statistical count of upcoming request for a specified resource
Backup, consistency issues

34. Content Replication and Placement
Client-initiated Replicas
Client cache
Local store facility
Managing the cache is left entirely to the client, in principle
Improve access times to data
Limited amount of time
Placement; local cache, cache server
Really needed? File servers, enhancements on network and system resources

35. Content Distribution State versus Operations
Propagate only a notification of an update.
Low bandwidth, effective
Transfer data from one copy to another.
Whole data, logs, log packages
Propagate the update operation to other copies.
No data, process time/complexity

36. Content Distribution Pull versus Push Protocols
Push, server based, server initiated
Pull, client based, send request
Hybrid model, leases

37. Content Distribution Unicasting versus Multicasting
Send a message for every client
Send message to entire system
Unicasting, pull based
Multicasting, push based

38. Consistency Protocols
Continuous Consistency
Primary-based Protocols
Replicated-write Protocols
Cache-coherence Protocols
Implementing Client-centric Consistency

39. Data-Centric Consistency Models
Introduction
Reasons for Replication
Replication as Scaling Technique
Data-Centric Consistency Models
Continuous Consistency
Consistent Ordering of Operations
Client-Centric Consistency Models
Eventual Consistency
Monotonic Reads
Monotonic Writes
Read Your Writes
Writes Follow Reads
Replica Management
Replica Server Placement
Content Replication and Placement
Content Distribution
Consistency Protocols