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With the average record size and the average number of accumulated records at a First Server being S cr and N cr (defined earlier), and the number of records used by the lookup process (N l ), then the value of H a can be approximated by (N N × N cr + N l ) × S cr.
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To boost the lookup process the design is enhanced by Bloom filter and Quotient filter.
Then it starts the lookup process in its intra-DHT.
The lookup process is carried out as follows.
The evaluations indicate that the excellent data structure and the efficient lookup algorithm of MaFIB can effectively reduce the memory cost and can accelerate the lookup process.
In particular, when HPM is combined with a broadcast mechanism, the lookup process can be significantly improved (four hops).
The MIHT is built out of tree-based data structures and the lookup process consists basically of traversing these structures.
The result to be sent to the DAC can be simply taken as an output value by a lookup process, or, to be more precise, the value can be interpolated from the neighbor values to get the output.
We start by looking at the impact of having different number of replicas as a way to speed up the resource lookup process.
More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hot-spot.
More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hotspot.
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CEO of Professional Science Editing for Scientists @ prosciediting.com