基于K-means的動態(tài)聚類垃圾回收算法研究

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中圖分類號：TP333；TP301.6 文獻(xiàn)標(biāo)識碼：A 文章編號：2096-4706（2025）07-0093-05

Abstract： Inconsumer electronics products based onNAND flash memory，garbage colection significantlyafects device performance，andcold-hot data separation is the keytooptimizing the garbage colection algorithm.This paper proposes an inovative method that uses the K-means clustering algorithm to automaticall determinethedata heat intervals and achieve eficientcold-hotdataseparation.Additionally，anadaptiveifluence factoradjustmentstrategyisdesignedtodyamically balancetheinfuenceofhistoricaldataheatandrecentdataheatOnthisbasis，thegarbagecolectionstrategyisoptimized.The simulationresults showthatcompared withtheexistingalgorithms，this methodhasasignifcant efectonimproving thereadwriteperformanceand achieving wear leveling，which is helpful in prolonging the service lifeoftheequipment and improving the overall performance.

Keywords： flash memory; garbage collection; cold-hot separation; K-means; clustering

0 引言

隨著移動設(shè)備、嵌入式系統(tǒng)和數(shù)據(jù)中心的普及，NANDFlash閃存在存儲領(lǐng)域的重要性日益凸顯。（剩余7766字）