▁the ▁one ▁for ▁dynamic ▁rep lication ▁has ▁been ▁designed ▁around ▁a ▁hier arch ical ▁rep lication ▁model ▁found ▁in ▁most ▁data ▁gr ids . ▁It ▁works ▁on ▁a ▁similar ▁algorithm ▁to ▁dynamic ▁rep lication ▁with ▁file ▁access ▁requests ▁being ▁a ▁prime ▁factor ▁in ▁determ ining ▁which ▁files ▁should ▁be ▁rep licated . ▁A ▁key ▁difference , ▁however , ▁is ▁the ▁number ▁and ▁frequency ▁of ▁rep lica ▁cre ations ▁is ▁key ed ▁to ▁a ▁dynamic ▁threshold ▁that ▁is ▁computed ▁based ▁on ▁request ▁arrival ▁rates ▁from ▁clients ▁over ▁a ▁period ▁of ▁time . ▁If ▁the ▁number ▁of ▁requests ▁on ▁average ▁exceed s ▁the ▁previous ▁threshold ▁and ▁shows ▁an ▁up ward ▁tr end , ▁and ▁storage ▁util ization ▁rates ▁indicate ▁capacity ▁to ▁create ▁more ▁rep lic as , ▁more ▁rep lic as ▁may ▁be ▁created . ▁As ▁with ▁dynamic ▁rep lication , ▁the ▁removal ▁of ▁rep lic as ▁that ▁have ▁a ▁lower ▁threshold ▁that ▁were ▁not ▁created ▁in ▁the ▁current ▁rep lication ▁interval ▁can ▁be ▁removed ▁to ▁make ▁space ▁for ▁the ▁new ▁rep lic as . ▁ ▁Fair - share ▁rep lication ▁Like ▁the ▁adapt ive ▁and ▁dynamic ▁rep lication ▁methods ▁before , ▁fair - share ▁rep lication ▁is ▁based ▁on ▁a ▁hier arch ical ▁rep lication ▁model . ▁Also , ▁like ▁the ▁two ▁before , ▁the ▁popular ity ▁of ▁files ▁play ▁a ▁key ▁role ▁in ▁determ ining ▁which ▁files ▁will ▁be ▁rep licated . ▁The ▁difference ▁with ▁this ▁method ▁is ▁the ▁pla cement ▁of ▁the ▁rep lic as ▁is ▁based ▁on ▁access ▁load ▁and ▁storage