Exploring Benefits of NVMe SSDs for BigData Processing in Enterprise Data Centers

Bayati, Mahsa; Bhimani, Janki; Mi, Ningfang

doi:10.1109/bigcom.2019.00024

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…This implementation also makes modifications to the virtual machine monitor (Xen\_Project 2015) and the guest's kernel. Bayati et al (2019) aims to evaluate Non-Volatile Memory Express (NVMe) devices in big data processing environments such as Apache Spark. To allows for testing NVMe related code without the need for buying expensive hardware they also propose a Quality of Service (QoS) aware NVMe emulator.…”

Section: Related Workmentioning

confidence: 99%

Freezing time emulating new and faster devices with virtual machines

et al. 2020

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Recent proposals of emerging data storage devices make it necessary to reevaluate all levels of the storage hierarchy to optimize the software stack performance. However, these new devices are not always widely available and therefore early experiments may be impossible. Emulators aim at mimicking as close as possible the behavior of a component, nonetheless, emulating new and fast storage devices is a challenging task due to time perception. In this work, we propose an approach to emulate storage devices using virtual machines (VMs) allowing the evaluation of a new device within a real system. We use a technique called freezing time, which pauses a VM to manipulate its clock and hide the real I/O completion time. Our approach is implemented at the hypervisor level and it is transparent to the guest operating system or application. We evaluate the technique under a real system using regular magnetic disks to emulate faster storage devices. Our method presented a latency error of 6.5% compared to a real device. Moreover, decoupled experiment between two laboratories, at the Barcelona Super Computing Center (BSC) in Spain, and the Center of Computer Science and Free Software (C3SL) in Brazil, demonstrated that our approach is reproducible and promising to allow the virtual evaluation of next-gen storage devices.

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Section: Related Workmentioning

confidence: 99%

Freezing time emulating new and faster devices with virtual machines

et al. 2020

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“…Nowadays, solid-state drives (SSD) represent the preferred backbone storage solution for either on-premise data centers or in-cloud computing facilities thanks to their low latency and high throughput capabilities compared to mechanical hard disk rrives (HDD) [4]. The positive performance is achieved through complex monitoring and dynamic management provided by a synergy between the SSD controller (i.e., the computing brain of a drive) and the integrated storage medium, namely the 3D NAND flash [5].…”

Section: Introductionmentioning

confidence: 99%

Modeling 3D NAND Flash with Nonparametric Inference on Regression Coefficients for Reliable Solid-State Storage

Borghesi,

Zambelli,

Micheloni

et al. 2023

Future Internet

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Solid-state drives represent the preferred backbone storage solution thanks to their low latency and high throughput capabilities compared to mechanical hard disk drives. The performance of a drive is intertwined with the reliability of the memories; hence, modeling their reliability is an important task to be performed as a support for storage system designers. In the literature, storage developers devise dedicated parametric statistical approaches to model the evolution of the memory’s error distribution through well-known statistical frameworks. Some of these well-founded reliability models have a deep connection with the 3D NAND flash technology. In fact, the more precise and accurate the model, the less the probability of incurring storage performance slowdowns. In this work, to avoid some limitations of the parametric methods, a non-parametric approach to test the model goodness-of-fit based on combined permutation tests is carried out. The results show that the electrical characterization of different memory blocks and pages tested provides an FBC feature that can be well-modeled using a multiple regression analysis.

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