IaaS load balancing and system redundancy
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Date 2020-03-16
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During cloud migration data can be lost in the process but due to redundancy, that w0ould not be a problem because it can still be recovered
in other systems. Also in cloud management of data is very crucial and it is advisable to always have a copy of the company’s data stored
somewhere just in case of malfunctioning of the system because when security issues occur in the cloud storage systems it can have many
effects on the company. So as to stay on the safe side, it is important for companies to duplicate their data to ensure security and confidence
that there is no point they will be able to lose their data completely which can be devastating (Maciel, et al, 2019). Load balancing in Action As
earlier stated, the main intention of load balancing is to ensure that there is equal dist4ibution of data traffic throughout the network thus
preventing overloading of data. For a company that would be considering migration to cloud computing, their cloud computing service
providers would be in a position to distribute their data traffic throughout their entire network to help curb the issue of data traffic and overload
that would lead to the system related errors and malfunctions (Nandy, et al, 2019). IaaS cloud would offer elementary resources of IT like
networking features, more flexibility and computing to the company. Because cloud computing offers storage according to the subscription, it
would be possible for a company to either upscale or downscale their data efficiently without any hitches. This is made possible through the
offsite data centers available. In the process of upscaling for instance, it is important that the data traffic that would now be highly generated,
be controlled. Cloud computing service providers provide elastic load balancers that ensure distributing of this new data traffic all over the
network and only to the healthy nodes. In the case of maintenance, the nodes will switch data traffic flow to the healthy nodes while providing
ample time for the maintenance of the unhealthy nodes. Once the unhealthy nodes have been repaired, it would now be possible to return the
data back to the healthy nodes, again, in a proportional distribution (Adhikari, et al, 2018).
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