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({K_{s}}) is the recency-based rank.
In other words, we can describe the two rank variables as: ({K_{s}}) is the recency-based rank.
(a) The recency-based rank of D1 has exponents (alpha_{K_{s}} approx 1.644) and exponential cut-off (kappa_{K_{s}}approx40.94), whereas the frequency-based rank distribution has a better fit for (alpha_{K_{f}}approx1.56) with (kappa_{K_{f}} approx23.6).
If the movement is selected to be a return, with probability (1 - alpha) the ith last visited location is selected from a Zipfian distribution (Zipf's law) with probability p(i propto K_{s}(l_{i})^{-eta}, where (K_{s}(l_{i})) is the recency-based rank of the location (l_{i}).
The new system will switch away from the current, recency-based system and instead rank comments according to a wide range of factors.
This observation justifies the higher byte hit rate achieved by LRU recency-based replacement policy in our BitTorrent caching simulation.
In the second part, the efficiency of the recency-based policy and a traditional ID-based policy is analyzed.
This paper concludes with recommendations on cascading and zoning the warehouse for applying the recency-based policy.
Utilizing the recurrent characteristics, a salient recency-based storage assignment policy and an associated cascaded warehouse configuration are proposed and analyzed in this paper.
Three representative cache replacement policies are considered in the simulation study: a recency-based policy called least-recently-used, a frequency-based policy called least-frequently-used-with-aging, and a size-based policy called greedy-dual-size.
For such, we propose a recency-based variation to the EPR model where the recency bias is incorporated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com