A Hybrid Non-Volatile Cache Design for Solid-State Drives Using Comprehensive I/O Characterization

Tarihi, Mojtaba; Asadi, Hossein; Haghdoost, Alireza; Arjomand, Mohammad; Sarbazi-Azad, Hamid

doi:10.1109/tc.2015.2455978

Cited by 27 publications

(25 citation statements)

References 27 publications

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“…Otherwise, we use the equation of NO-MIG. Therefore, the hit probability of DRAM can be calculated using Equation 6.…”

Section: Mig-probmentioning

confidence: 99%

“…The migration probability, however, affects P (bef ore|hit) since the distribution of stack distance is dependent on the migration probability. Therefore, probArr is computed with respect to Equation 6. In addition, the probabilities in probArr are calculated as P (i|hit) for an arbitrary position i, which enables us to sum their values without the need for additional calculations.…”

Section: Before/aftermentioning

confidence: 99%

“…The average error of estimating the NVM lifetime is 2.93%, while the maximum error is 8.8%, which is lower than the error in estimating hit ratio and AMAT. 6. Appendix B presents the absolute error values to estimate AMAT.…”

Section: Accuracymentioning

confidence: 99%

“…To address the shortcomings of DRAM, emerging Non-Volatile Memories (NVMs) offer promising characteristics, such as low idle power, high density, and non-volatility [3], [4], [5], [6], [7], [8], [9]. Example emerging NVM technologies include Phase Change Memory (PCM), Spin-Transfer Torque Magnetic Random-Access Memory (STT-MRAM), Metal Oxide Resistive RAM, and memristors.…”

Section: Introductionmentioning

confidence: 99%

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

Salkhordeh

Mutlu

Asadi

2019

IEEE Trans. Comput.

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Emerging Non-Volatile Memories (NVMs) have promising advantages (e.g., lower idle power, higher density, and nonvolatility) over the existing predominant main memory technology, DRAM. Yet, NVMs also have disadvantages (e.g., longer latencies, higher active power, and limited endurance). System architects are therefore examining hybrid DRAM-NVM main memories to enable the advantages of NVMs while avoiding the disadvantages as much as possible. Unfortunately, the hybrid memory design space is very large and complex due to the existence of very different types of NVMs and their rapidly-changing characteristics. Therefore, optimization of performance and lifetime of hybrid memory based computing platforms and their experimental evaluation using traditional simulation methods can be very time-consuming and sometimes even impractical. As such, it is necessary to develop a fast and flexible analytical model to estimate the performance and lifetime of hybrid memories on various workloads. This paper presents an analytical model for hybrid memories based on Markov decision processes. The proposed model estimates the hit ratio and lifetime for various configurations of DRAM-NVM hybrid main memories. Our model also provides accurate estimation of the effect of data migration policies on the hybrid memory hit ratio (i.e., percentage of accesses supplied by either DRAM or NVM), one of the most important factors in hybrid memory performance and lifetime. Such an analytical model can aid designers to tune hybrid memory configurations to improve performance and/or lifetime. We present several optimizations that make our model more efficient while maintaining its accuracy. Our experimental evaluations conducted using the PARSEC benchmark suite show that the proposed model (a) accurately predicts the hybrid memory hit ratio compared to the state-of-the-art hybrid memory simulators with an average (maximum) error of 4.61% (13.6%) on a commodity server (equipped with 192GB main memory and quad-core Xeon processor), (b) accurately estimates the NVM lifetime with an average (maximum) error of 2.93% (8.8%), and (c) is on average (up to) 4x (10x) faster than conventional state-of-the-art simulation platforms for hybrid memories.

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“…Otherwise, we use the equation of NO-MIG. Therefore, the hit probability of DRAM can be calculated using Equation 6.…”

Section: Mig-probmentioning

confidence: 99%

Section: Before/aftermentioning

confidence: 99%

Section: Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Salkhordeh

Mutlu

Asadi

2019

IEEE Trans. Comput.

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“…The error of MC simulations is inversely proportional to the root square of the number of iterations as shown in Equation 10. The number of iterations can be adjusted by the target accuracy (error) and the given confidence level.…”

Section: E Monte Carlo Simulationmentioning

confidence: 99%

Modeling Impact of Human Errors on the Data Unavailability and Data Loss of Storage Systems

Kishani

Asadi

2018

IEEE Trans. Rel.

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Data storage systems and their availability play a crucial role in contemporary datacenters. Despite using mechanisms such as automatic fail-over in datacenters, the role of human agents and consequently their destructive errors is inevitable. Due to very large number of disk drives used in exascale datacenters and their high failure rates, the disk subsystem in storage systems has become a major source of Data Unavailability (DU) and Data Loss (DL) initiated by human errors. In this paper, we investigate the effect of Incorrect Disk Replacement Service (IDRS) on the availability and reliability of data storage systems. To this end, we analyze the consequences of IDRS in a disk array, and conduct Monte Carlo simulations to evaluate DU and DL during mission time. The proposed modeling framework can cope with a) different storage array configurations and b) Data Object Survivability (DOS), representing the effect of system level redundancies such as remote backups and mirrors. In the proposed framework, the model parameters are obtained from industrial and scientific reports alongside field data which have been extracted from a datacenter operating with 70 storage racks. The results show that ignoring the impact of IDRS leads to unavailability underestimation by up to three orders of magnitude. Moreover, our study suggests that by considering the effect of human errors, the conventional beliefs about the dependability of different Redundant Array of Independent Disks (RAID) mechanisms should be revised. The results show that RAID1 can result in lower availability compared to RAID5 in the presence of human errors. The results also show that employing automatic fail-over policy (using hot spare disks) can reduce the drastic impacts of human errors by two orders of magnitude.

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Survey on Energy-Saving Technologies for Disk-Based Storage Systems

Wang

Sun

et al. 2017

Algorithms and Architectures for Parallel Processing

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A Hybrid Non-Volatile Cache Design for Solid-State Drives Using Comprehensive I/O Characterization

Cited by 27 publications

References 27 publications

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

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

Modeling Impact of Human Errors on the Data Unavailability and Data Loss of Storage Systems

Survey on Energy-Saving Technologies for Disk-Based Storage Systems

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