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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. == Terminology == One speaks of: * ''data replication'' if the same data is stored on multiple storage devices, * ''computation replication'' if the same computing task is executed many times. A computational task is typically ''replicated in space'', i.e. executed on separate devices, or it could be ''replicated in time'', if it is executed repeatedly on a single device. Replication in space or in time is often linked to scheduling algorithms 〔Mansouri, Najme, GholamHosein Dastghaibyfard, and Ehsan Mansouri. "Combination of data replication and scheduling algorithm for improving data availability in Data Grids." Journal of Network and Computer Applications (2013)〕 The 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. Also, in a failure scenario, a failover of replicas is hidden as much as possible. The latter refers to data replication with respect to Quality of Service (QoS) aspects.〔V. Andronikou, K. Mamouras, K. Tserpes, D. Kyriazis, T. Varvarigou, ''Dynamic QoS-aware Data Replication in Grid Environments'', Elsevier Future Generation Computer Systems - The International Journal of Grid Computing and eScience, 2012〕 Computer scientists talk about active and passive replication in systems that replicate data or services: * ''active replication'' is performed by processing the same request at every replica. * ''passive replication'' involves processing each single request on a single replica and then transferring its resultant state to the other replicas. If at any time one master replica is designated to process all the requests, then we are talking about the ''primary-backup'' scheme (''master-slave'' scheme) predominant in high-availability clusters. On the other side, if any replica processes a request and then distributes a new state, then this is a ''multi-primary'' scheme (called ''multi-master'' in the database field). In the multi-primary scheme, some form of distributed concurrency control must be used, such as distributed lock manager. Load balancing differs from task replication, since it distributes a load of different (not the same) 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. Backup differs from replication in that it saves a copy of data unchanged for a long period of time. Replicas, on the other hand, undergo frequent updates and quickly lose any historical state. Replication is one of the oldest and most important topics in the overall area of distributed systems. Whether one replicates data or computation, the objective is to have some group of processes that handle incoming events. If we replicate data, these processes are passive and operate only to maintain the stored data, reply to read requests, and apply updates. When we replicate computation, the usual goal is to provide fault-tolerance. For example, a replicated service might be used to control a telephone switch, with the objective of ensuring that even if the primary controller fails, the backup can take over its functions. But the underlying needs are the same in both cases: by ensuring that the replicas see the same events in equivalent orders, they stay in consistent states and hence any replica can respond to queries. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Replication (computing)」の詳細全文を読む スポンサード リンク
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