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It estimates minimal threshold time gap (to) that must fall between read and update operation on any N- replicated data object which ensures correctness in next read operation.
There exists a relationship between the number of replicas (Rp) and time gap between read and update (tg) on a data object O, which is shown in Fig. 5.
As discussed in the previous section, the time gap between read and update request on a replicated data object is dynamically configurable and tunable predictor of CI, which is efficiently employed so that read does not occur in the unsafe period.
Similar(57)
As these ONDMs follow a similar design, the introduced overhead is roughly comparable: respectively the write, read and update overhead ranges between [ 4−14%], [ 4−21%] and [ 60−194%] (on a cluster setup).
Table 4 shows the results for write, read and update.
Cloud Serving Benchmark (YCSB), specifically for create, read and update, and most notably search operations.
We answer RQ1 by running the benchmarks discussed above for the create, read and update operations.
Table 3 shows the overhead for write, read and update operations.
The input workload to a CICT is a series of read and update operations.
The write, read and update performance overhead can be considered significant.
A replicated data object undergoes numerous reads and updates in the system.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com