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NVMs have promising advantages (e.g., lower idle power, higher density) overthe existing predominant main memory technology, DRAM. Yet, NVMs also havedisadvantages (e.g., limited endurance). System architects are thereforeexamining hybrid DRAM-NVM main memories to enable the advantages of NVMs whileavoiding the disadvantages as much as possible. Unfortunately, the hybridmemory design space is very large and complex due to the existence of verydifferent types of NVMs and their rapidly-changing characteristics. Therefore,optimization of performance and lifetime of hybrid memory based computingplatforms and their experimental evaluation using traditional simulationmethods can be very time-consuming and sometimes even impractical. As such, itis necessary to develop a fast and flexible analytical model to estimate theperformance and lifetime of hybrid memories on various workloads. This paperpresents an analytical model for hybrid memories based on Markov decisionprocesses. The proposed model estimates the hit ratio and lifetime for variousconfigurations of DRAM-NVM hybrid main memories. Our model also providesaccurate estimation of the effect of data migration policies on the hybridmemory hit ratio, one of the most important factors in hybrid memoryperformance and lifetime. Such an analytical model can aid designers to tunehybrid memory configurations to improve performance and/or lifetime. We presentseveral optimizations that make our model more efficient while maintaining itsaccuracy. Our experimental evaluations show that the proposed model (a)accurately predicts the hybrid memory hit ratio with an average error of 4.61%on a commodity server, (b) accurately estimates the NVM lifetime with anaverage error of 2.93%, and (c) is on average 4x faster than conventionalstate-of-the-art simulation platforms for hybrid memories.

An Analytical Model for Performance and Lifetime Estimation of Hybrid DRAM-NVM Main Memories