Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
Replication in computing can refer to:
Data replication, where the same data is stored on multiple storage devices
Computation replication, where the same computing task is executed many times. Computational tasks may be:
Replicated in space, where tasks are executed on separate devices
Replicated in time, where tasks are executed repeatedly on a single device
Replication in space or in time is often linked to scheduling algorithms.
Access to a replicated entity is typically uniform with access to a single non-replicated entity. The replication itself should be transparent to an external user. In a failure scenario, a failover of replicas should be hidden as much as possible with respect to quality of service.
Computer scientists further describe replication as being either:
Active replication, which is performed by processing the same request at every replica
Passive replication, which involves processing every request on a single replica and transferring the result to the other replicas
When one leader replica is designated via leader election to process all the requests, the system is using a primary-backup or primary-replica scheme, which is predominant in high-availability clusters. In comparison, if any replica can process a request and distribute a new state, the system is using a multi-primary or multi-master scheme. In the latter case, some form of distributed concurrency control must be used, such as a distributed lock manager.
Load balancing differs from task replication, since it distributes a load of different computations across machines, and allows a single computation to be dropped in case of failure. Load balancing, however, sometimes uses data replication (especially multi-master replication) internally, to distribute its data among machines.
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