▁approach ▁to ▁pla cement ▁of ▁rep lic as ▁based ▁on ▁popular ity ▁of ▁the ▁data . ▁The ▁method ▁has ▁been ▁designed ▁around ▁a ▁hier arch ical ▁rep lication ▁model . ▁The ▁data ▁management ▁system ▁keeps ▁track ▁of ▁available ▁storage ▁on ▁all ▁nodes . ▁It ▁also ▁keeps ▁track ▁of ▁requests ▁( h its ) ▁for ▁which ▁data ▁clients ▁( users ) ▁in ▁a ▁site ▁are ▁request ing . ▁When ▁the ▁number ▁of ▁hits ▁for ▁a ▁specific ▁dataset ▁exceed s ▁the ▁rep lication ▁threshold ▁it ▁triggers ▁the ▁creation ▁of ▁a ▁rep lica ▁on ▁the ▁server ▁that ▁directly ▁services ▁the ▁user ’ s ▁client . ▁If ▁the ▁direct ▁serv icing ▁server ▁known ▁as ▁a ▁father ▁does ▁not ▁have ▁sufficient ▁space , ▁then ▁the ▁father ’ s ▁father ▁in ▁the ▁hierarchy ▁is ▁then ▁the ▁target ▁to ▁receive ▁a ▁rep lica ▁and ▁so ▁on ▁up ▁the ▁chain ▁until ▁it ▁is ▁exha usted . ▁The ▁data ▁management ▁system ▁algorithm ▁also ▁allows ▁for ▁the ▁dynamic ▁delet ion ▁of ▁rep lic as ▁that ▁have ▁a ▁null ▁access ▁value ▁or ▁a ▁value ▁lower ▁than ▁the ▁frequency ▁of ▁the ▁data ▁to ▁be ▁stored ▁to ▁free ▁up ▁space . ▁This ▁impro ves ▁system ▁performance ▁in ▁terms ▁of ▁response ▁time , ▁number ▁of ▁rep lic as ▁and ▁helps ▁load ▁balance ▁across ▁the ▁data ▁grid . ▁This ▁method ▁can ▁also ▁use ▁dynamic ▁algorithms ▁that ▁determine ▁whether ▁the ▁cost ▁of ▁creating ▁the ▁rep lica ▁is ▁truly ▁worth ▁the ▁expected ▁g ains ▁given ▁the ▁location . ▁ ▁Ada pt ive ▁rep lication ▁This ▁method ▁of ▁rep lication ▁like