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Bridging the Gap between Application and Solid-State-Drives

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Date Issued:
2018
Abstract/Description:
Data storage is one of the important and often critical parts of the computing systemin terms of performance, cost, reliability, and energy.Numerous new memory technologies,such as NAND flash, phase change memory (PCM), magnetic RAM (STT-RAM) and Memristor,have emerged recently.Many of them have already entered the production system.Traditional storage optimization and caching algorithms are far from optimalbecause storage I/Os do not show simple locality.To provide optimal storage we need accurate predictions of I/O behavior.However, the workloads are increasingly dynamic and diverse,making the long and short time I/O prediction challenge.Because of the evolution of the storage technologiesand the increasing diversity of workloads,the storage software is becoming more and more complex.For example, Flash Translation Layer (FTL) is added for NAND-flash based Solid State Disks (NAND-SSDs).However, it introduces overhead such as address translation delay and garbage collection costs.There are many recent studies aim to address the overhead.Unfortunately, there is no one-size-fits-all solution due to the variety of workloads.Despite rapidly evolving in storage technologies,the increasing heterogeneity and diversity in machines and workloadscoupled with the continued data explosionexacerbate the gap between computing and storage speeds.In this dissertation, we improve the data storage performance from both top-down and bottom-up approach.First, we will investigate exposing the storage level parallelismso that applications can avoid I/O contentions and workloads skewwhen scheduling the jobs.Second, we will study how architecture aware task scheduling can improve the performance of the application when PCM based NVRAM are equipped.Third, we will develop an I/O correlation aware flash translation layer for NAND-flash based Solid State Disks.Fourth, we will build a DRAM-based correlation aware FTL emulator and study the performance in various filesystems.
Title: Bridging the Gap between Application and Solid-State-Drives.
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Name(s): Zhou, Jian, Author
Wang, Jun, Committee Chair
Lin, Mingjie, Committee Member
Fan, Deliang, Committee Member
Ewetz, Rickard, Committee Member
Qi, GuoJun, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2018
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Data storage is one of the important and often critical parts of the computing systemin terms of performance, cost, reliability, and energy.Numerous new memory technologies,such as NAND flash, phase change memory (PCM), magnetic RAM (STT-RAM) and Memristor,have emerged recently.Many of them have already entered the production system.Traditional storage optimization and caching algorithms are far from optimalbecause storage I/Os do not show simple locality.To provide optimal storage we need accurate predictions of I/O behavior.However, the workloads are increasingly dynamic and diverse,making the long and short time I/O prediction challenge.Because of the evolution of the storage technologiesand the increasing diversity of workloads,the storage software is becoming more and more complex.For example, Flash Translation Layer (FTL) is added for NAND-flash based Solid State Disks (NAND-SSDs).However, it introduces overhead such as address translation delay and garbage collection costs.There are many recent studies aim to address the overhead.Unfortunately, there is no one-size-fits-all solution due to the variety of workloads.Despite rapidly evolving in storage technologies,the increasing heterogeneity and diversity in machines and workloadscoupled with the continued data explosionexacerbate the gap between computing and storage speeds.In this dissertation, we improve the data storage performance from both top-down and bottom-up approach.First, we will investigate exposing the storage level parallelismso that applications can avoid I/O contentions and workloads skewwhen scheduling the jobs.Second, we will study how architecture aware task scheduling can improve the performance of the application when PCM based NVRAM are equipped.Third, we will develop an I/O correlation aware flash translation layer for NAND-flash based Solid State Disks.Fourth, we will build a DRAM-based correlation aware FTL emulator and study the performance in various filesystems.
Identifier: CFE0007273 (IID), ucf:52188 (fedora)
Note(s): 2018-08-01
Ph.D.
Engineering and Computer Science, Electrical Engineering and Computer Engineering
Doctoral
This record was generated from author submitted information.
Subject(s): Solid-State-Drives -- Approximative Computing -- Flash Translation Layer -- Data Correlation
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007273
Restrictions on Access: public 2018-08-15
Host Institution: UCF

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