ML-powered backups for business continuity

ML-powered backups for business continuity
[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”3.22″][et_pb_row admin_label=”row” _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text admin_label=”Text” _builder_version=”3.27.4″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”]

Artificial Intelligence has had an impact on almost every facet of business and business continuity is no exception.

As data backups are generally the first step taken by data-driven businesses who implement AI systems, flexibility and capacity are of a prime concern. 3dotDigital suggests that business continuity implementations should include options for ensuring network availability. It is the smart high availability technology that ensures businesses are available. Supplementing the backups for business continuity with intelligent algorithms that require light-level machine learning and making use of the rules-based aspect of artificial intelligence that enable intelligent high availability, businesses can significantly reduce the amount of data loss.

AI-enabled backups

Policy-based backups over job-based backups:

Conventionally, the backing up of servers, virtual machines and applications is done on a job-based model wherein backup jobs are scheduled as per a pre-set sequence and at a pre-set time. However, the computing landscape has changed considerably in the recent past including decentralization and use of mobile devices – be it laptops, tablets or smartphones; the job-based model is less effective now.

In a job-based model, if a mobile device or for that matter a storage device is not online at the specific time of the backup job, it will not get backed up until the next job that could be several days after. This is where AI comes to the rescue as users can come up with better backup policies using the intelligent algorithms of statistical artificial intelligence. The backend intelligence of the policy-based approach allows businesses to customise their backups as per the machines they wish to attend, the frequency and where they wish to backup.

Non-sequential backups and preferential backup hierarchy:

The combination of machine learning and static algorithms enables non sequential backups and preferential backup hierarchy. Non-sequential backups mean taking backups in different sequences depending on the availability of the devices which will vary for mobile devices like laptops, tablets, and smartphones. If a particular laptop is not online at the time of daily backup, the system will back up another and keep looking if the first laptop has come online.

The good part about artificial intelligence with its decision-making abilities powering the systems is that it enables in determining which backups are more important in terms of priority over others. For example, if a laptop has not been online at the daily backup time for five days and there is another laptop that has been offline for two days, the backup policy would give priority to the first one. This proactiveness achieved through AI sets priority amidst the list of devices that need to be backed up and targets devices that have been most out of the policy first over others.

Another important facet of the machine learning used in AI-powered smart backups for business continuity is setting up the backup job priority as per the time taken. For example, if two backups are to be done simultaneously and it takes one unit to get backed up in 15 minutes and the other to get backed up in 45 minutes, then the first unit taking lesser time will get done first so that there is more time to do the other later.

AI enables systems to dynamically learn over time and improve the processes which may not be consistent owing to an increasingly large percentage of mobile devices. Based on the need of the user, the algorithms are able to adjust how they go about their jobs. In these use cases, machine learning keeps evaluating when the devices to be backed up come online, what would be the data consumption, which ones can get done quickly and hence should be done first before moving on to others that might take longer.

Smart Availability

The policies for network availability and the rules they are made up of demonstrate the knowledge base aspect of AI. These rules are at the base of automation for failovers and dynamic load balancing. Businesses need to define the rules regarding how the business will be run and the automation will be done while the technology implements those rules. This will enable businesses to change over from on-premises settings to the cloud and back and even between different servers ensuring that the networks remain available all the time. Businesses can make use of this approach to transfer workloads where they are most feasible for computing. The success lies in doing this with dedicated rules i.e. if a work unit fails for some reason, the technology intelligence steps in sans any human intervention and another device gets started or the workload gets transferred onto another device to ensure that the business functions are not affected.

It is the coming together of business continuity abilities of backups, and intelligent high availability technologies, that has enabled businesses to enhance their capacity to keep the data protected and networks available.

[/et_pb_text][et_pb_text _builder_version=”4.0.9″ hover_enabled=”0″]

Do read our other blogs on business continuity

(1) Business Continuity: Fighting back interruptions during COVID-19 pandemic
(2) Business continuity priorities considering the Coronavirus outbreak 
(3) Is your team equipped for remote work?


Share this post