Editing Consistency model (section)

==== Replicated-write protocols ====

In replicated-write protocols,<ref name="Replica"/> unlike the primary-based protocol, all updates are carried out to all replicas.

===== Active replication =====

In active replication,<ref name="Replica"/> there is a process associated with each replica to perform the write operation. In other words, updates are sent to each replica in the form of an operation in order to be executed. All updates need to be performed in the same order in all replicas. As a result, a totally-ordered multicast mechanism is required. There is a scalability issue in implementing such a multicasting mechanism in large distributed systems. There is another approach in which each operation is sent to a central coordinator (sequencer). The coordinator first assigns a sequence number to each operation and then forwards the operation to all replicas. Second approach cannot also solve the scalability problem.

===== Quorum-based protocols =====

Voting can be another approach in replicated-write protocols. In this approach, a client requests and receives permission from multiple servers in order to read and write a replicated data. As an example, suppose in a distributed file system, a file  is replicated on N servers. To update a file, a client must send a request to at least {{nowrap|N/2 + 1}} in order to make their agreement to perform an update. After the agreement, changes are applied on the file and a new version number is assigned to the updated file. Similarly, for reading replicated file, a client sends a request to {{nowrap|N/2 + 1}} servers in order to receive the associated version number from those servers. Read operation is completed if all received version numbers are the most recent version.<ref name="Replica"/>

===== Cache-coherence protocols =====

In a replicated file system, a cache-coherence protocol<ref name="Replica"/> provides the cache consistency while caches are generally controlled by clients. In many approaches, cache consistency is provided by the underlying hardware. Some other approaches in middleware-based distributed systems apply software-based solutions to provide the cache consistency.

Cache consistency models can differ in their coherence detection strategies that define when inconsistencies occur. There are two approaches to detect the inconsistency; static and dynamic solutions. In the static solution, a compiler determines which variables can cause the cache inconsistency. So, the compiler enforces an instruction in order to avoid the inconsistency problem. In the dynamic solution, the server checks for inconsistencies at run time to control the consistency of the cached data that has changed after it was cached.

The coherence enforcement strategy is another cache-coherence protocol. It defines  ''how'' to provide the consistency in caches by using the copies located on the server. One way to keep the data consistent is to never cache the shared data. A server can keep the data and apply some consistency protocol such as primary-based protocols to ensure the consistency of shared data. In this solution, only private data can be cached by clients. In the case that shared data is cached, there are two approaches in order to enforce the cache coherence.

In the first approach, when a shared data is updated, the server forwards invalidation to all caches. In the second approach, an update is propagated. Most caching systems apply these two approaches or dynamically choose between them.