What is redundancy in computer science?

When operating relevant IT systems and handling critical data sets, intentional redundancy should be an integral part of danger prevention and data protection. Systems are redundant when the same technical components and data sets are available several times in parallel, protecting against loss and failure. Redundancy does not only have advantages in terms of storage capacity and IT equipment, however.

The noun “redundancy” or the adjective “redundant” and its meaning go back to the Latin “redundare”. It is composed of “re” for “back” and “unda” for “wave”, and it denotes something that exists in abundance, i.e., also something that exists multiple times and in excess. While “redundant” is barely found in everyday language, so-called redundancy plays a far greater role in computer science.

When we talk about redundancy in IT, we are talking about data center security and system availability. It refers to data and system components that are numerous, parallel, duplicated or mirrored and are thus available in abundance. Depending on the context, this can have both positive and negative connotations in IT. Redundancy in the positive sense stands for system-critical data sets that exist multiple times or are distributed across multiple servers. Redundancy, when negatively connoted, is found in the form of unintentional data duplicates that occupy storage space.

First, then, a distinction must be made between unintended and intended redundancy:

Accidental or unintentional redundancy in an organization’s systems or data centers is found in the form of multiple data sets stored in one site or distributed across multiple sites. Data duplication makes maintaining records more complicated and leads to data anomalies. Inconsistencies are the result as it is not clear which data should be accessed or which records are current. In addition, unintentional redundancies occupy important storage space and consume energy unnecessarily. This is prevented by normalization of databases.

Intended redundancy means the planned, repeated design of technical components to secure a server, strengthen supply paths and protect system/company-critical data. A distinction can be made between the following concepts in redundancy:

• Functional redundancy: Multiple and/or parallel technical system components mostly within one plant.
• Georedundancy: Locally separated, multiple data centers or data sets.
• Data redundancy: Multiple backed up, mirrored or parallel data sets

If hardware damage, system failures or cyberattacks occur, companies can protect themselves against data loss and the failure of business-critical processes through redundancy. Data is stored multiple times, and consistently in different locations, while important components such as supply routes for energy and air conditioning are at least duplicated.

Depending on the components installed, a distinction is made between:

• Homogeneous redundancy: Repeated design of components that are identical in terms of manufacturer technology. The disadvantage of this is that the similarity of the components means that the risk of an overall failure due to manufacturer errors or attacks directed at the manufacturer remains high.
• Diverse redundancy: Repeated design of components that differ by manufacturer, function, and type, making general system failures, uniform wear, and manufacturer-related failures less likely.

Redundancy as a criterion for system properties and system security exists in the following forms:

• Redundant technical components: System components of computers and networks, such as air conditioning, supply lines and servers, which take over the tasks of failed systems and components through multiple design or serve as a backup; this applies both to technical components within a system and to entire data centers that are available multiple times at different locations through geo-redundancy.
• Redundant information: Superfluous, unnecessary, obsolete, or duplicate records that are not relevant to the system and usually occupy storage space unintentionally.
• Redundant data: Repeated, mirrored, or distributed data sets across multiple sites and servers that prevent complete data loss in the event of failure or damage through RAID capabilities, backups, virtualization, or mirroring; serve as backup or disaster recovery; or provide faster access at a distance.
• Redundant bits: Added as extra bits during data transfers to prevent data loss during the transfer.

If it is important for companies to be able to access their servers without interruption, they will implement a computer network. This consists of redundant servers made of cluster systems with several nodes. In a network, each associated computer has equal access to existing databases and can take over access functions to critical data and applications for failed servers in the event of an emergency. To this end, a fail-safe is almost entirely guaranteed. Operational upkeep, launching without physical hard disks using network storage, and the replacement of faulty servers can all be achieved without interrupting business processes thanks to distributed computer capacities.

Depending on the concept, redundant servers can be divided into two modes:

• Active/active cluster (symmetric): In the context of active/active clusters, servers operate in live mode as cluster nodes in which multiple computers work in parallel with distributed power or independently of each other. In the event of a failure, the computing capacity is distributed to other servers in the network.
• Active/passive cluster (asymmetric): Active/passive clusters are called failover clusters and stand for the presence of redundant servers or network services that are in standby mode as a backup system and, by way of a switch-over, assume the functions of the main system in the event of a one-sided failure. This is automated by cluster manager/load balancer software. This also enables operational maintenance without any loss in performance.

To implement redundancy in network systems and computing facilities, there are various forms and concepts:

RAID stands for “Redundant Array of Independent Disks” and refers to various physical storage devices (RAID arrays) that are joined together to form a partition. In this way, the consistency and integrity of data sets is maintained even where an error occurs, and components can be exchanged without loss. This is done, among other things, by mirroring hard disks or by using parities with distributed data in the array. However, RAID systems should always be used in conjunction with an independent backup of all critical data.

As already shown with the example of active/active or active/passive clusters, a computer network as a high-availability or load-balancing cluster offers greater availability, load distribution and fail-safety through eliminated single points of failure and continuous processes.

Georedundancy is often found as a redundancy concept in the clustering of computing systems. It is used when particularly critical systems need to be protected against failure. In this case, identical computing systems are constructed locally and separately from each other, and data, geographically, is stored independently. If one data center fails, the redundant data center can assume each task or completely recover the data sets. Optimal geo-redundancy exists when additional data backups are available in further facilities.

Snapshots are virtual images or snapshots of hard disks and enable data and system conditions to be backed up redundantly in other storage centers. In the event of data loss at one location, data recovery can be implemented, therefore. Storage requirements for snapshots are significantly lower than for data copies because they are reference markers for data storage locations and not actual copies.

A backup requires more storage volume compared to snapshots as the data is copied and stored as a backup copy and in redundant form. Thanks to this data redundancy, the complete data set can be restored. Even with a redundant computer network, an additional backup is always recommended.

With CDP, data is stored as part of a continuous backup, which monitors changes and automatically updates them in the backup. It is therefore a data redundancy that backs up critical data in real time and protects against failures.

The advantages of intentional redundancy are obvious: Systems and networks that have multiple technical components and storage devices offer greater resilience, faster data access and more sustainable operations. Data recovery and continuity are ensured even in the event of severe failures. The disadvantage of redundant systems is the relatively high cost of several components, required storage space, and for the continuous updating of data copies.

Nevertheless, redundancy in the data center is becoming increasingly important in view of new cyber threats, outdated system technology and strict data protection requirements. Both end users and data center operators should take care not only to integrate failover and system security efficiently through redundant concepts, but also to mark them as a competitive advantage and USP through data center tiers.