Modeling Disaster Recovery By Estimating Rpo And Rto
Modelling Disaster Recovery by Estimating RPO and RTO Thaila Annamalai (CSU ID: 830860017),
[email protected]
Abstract Disaster Recovery (DR) proposes strategies for choosing the pattern of DR for every
business Unit. The importance of the business and the DR readiness is assessed by the recovery time
objective (RTO) and recovery point objective (RPO). This assessing method make sure business
continuity even under a drastic failure and a long disrupted period. The data backup and protection has
become a basic requirement in networks because of the generation of data in huge volume from each
business unit. The disasters that can occur has to be identified and should be evaluated ... Show more
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Traditionally, the DRP was to back up the data in a remote place and then access them during disaster.
The storing of data remotely can take place in two ways either periodically or from time to time.
These methods were costly and inefficient since large amount of storage was needed. The
development in cloud computing paved a way for this problem. In cloud computing large amount of
data can be generated and stored. Also, this gives real time backup of data. Though only large scale
business units use real time backup other advantages of cloud computing are utilized by other business
units. The primary working site and the backup site is separated. The probability of failure occurring
in both sites at the same time is rare. The RTO is defined as the time during which the business unit is
unavailable due to the failure. This includes the time of the disaster occurrence and the time needed to
restore the functionalities. The RPO is the time period between two successive backups. This metric
will let the user aware of the maximum amount of data that can be lost when the restoration is
completed. The RPO should be close to 0 in an optimized DR system.
MOTIVATION
Disaster recovery has become the topic of interest in today s research due to two main reasons: (1) The
size of the computer data handled by the organizations are huge and are growing with time. (2) if the
data loss is not handled within the
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