Consistency And Replication
Ömer Faruk SARAÇ
115112005
Outline
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Introduction
– Reasons for Replication
– Replication as Scaling Technique
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Data-Centric Consistency Models
– Continuous Consistency
– Consistent Ordering of Operations
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Client-Centric Consistency Models
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Eventual Consistency
Monotonic Reads
Monotonic Writes
Read Your Writes
Writes Follow Reads
Replica Management
– Replica Server Placement
– Content Replication and Placement
– Content Distribution
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Consistency Protocols
Reasons for Replication
• Enhance Reliability
• Improve Performance
– Scaling in numbers
– Scaling in geographical area
– Caching
Replication as Scaling Technique
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Placing replicas(data) close to clients
Network bandwidth issue
Cache
How to keep replicas consistent?
Loosen constraints
•
Introduction
– Reasons for Replication
– Replication as Scaling Technique
•
Data-Centric Consistency Models
– Continuous Consistency
– Consistent Ordering of Operations
•
Client-Centric Consistency Models
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Eventual Consistency
Monotonic Reads
Monotonic Writes
Read Your Writes
Writes Follow Reads
Replica Management
– Replica Server Placement
– Content Replication and Placement
– Content Distribution
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Consistency Protocols
Data-Centric Consistency
• Data store
– Shared data
– Shared memory
– Shared database
– Distributed file system
• Contracts between processes(clients) and
data store(replicas)
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
Continuous Consistency
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
Consistent Ordering of Operations
• Sequential Consistency
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
Consistent Ordering of Operations
• Casual Consistency
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
Consistent Ordering of Operations
• Grouping Operations
•
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
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
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
Eventual Consistency
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
Monotonic Reads
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
Monotonic Writes
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
Read Your Writes
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
Writes Follows Reads
•
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
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
Replica Server Placement
• Simple issue: money
• Choose best places K in set of possible places N where
K<N
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Network latency
Bandwidth
Physical distance
İgnore client location, use network topology
• Defining regions
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Dence cells
Cell, two dimensional rectangle
Too small, many replicas in a cell
Too large, too few clusters for a cell
Replica Server Placement
Content Replication and Placement
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
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
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
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
Content Distribution
• Pull versus Push Protocols
– Push, server based, server initiated
– Pull, client based, send request
– Hybrid model, leases
Content Distribution
• Unicasting versus Multicasting
– Send a message for every client
– Send message to entire system
– Unicasting, pull based
– Multicasting, push based
Consistency Protocols
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Continuous Consistency
Primary-based Protocols
Replicated-write Protocols
Cache-coherence Protocols
Implementing Client-centric Consistency
•
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