Achieving Best Price/Performance for Enterprise Grade Achieving Best Price/Performance for Enterprise Grade SDS with SPDK ... SSD Linux/SPDK Scale out Scale Up Scale out Node 0 Node

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  • Copyright 2017 FUJITSU LIMITED

    Achieving Best Price/Performance for Enterprise Grade SDS with SPDK

    Paul von Stamwitz

    0

  • Synopsis

    The Enterprise Storage market is rapidly migrating to NVMe-based all-flash configurations.

    However, CPU processing power is falling behind the performance gains in storage, creating inefficiencies in both performance and cost.

    To address this, Fujitsu is using SPDK toward the goal of achieving both higher performance and an overall lower cost per IOP for mission-critical workloads.

    Copyright 2017 FUJITSU LIMITED1

  • Agenda

    Background

    Why SPDK

    How Fujitsu is using SPDK

    What are we interested in

    Copyright 2017 FUJITSU LIMITED2

  • Copyright 2017 FUJITSU LIMITED

    ETERNUS protects customer assets forever and ensures continuous business operation

    Accelerating data flows with flash storage

    Effectively consolidating storage systems

    Managing unpredictable data growth with software defined storage

    Radically simplifying backup infrastructures

    Fujitsu Storage ETERNUS lineup

    All-flash system

    ETERNUS AF

    Hybrid RAID Storage

    ETERNUS DX

    Software defined hyper-scale storage

    ETERNUS CD10000

    Backup Appliances

    ETERNUS CS

    Tape Libraries

    ETERNUS LT

    3

  • Copyright 2017 FUJITSU LIMITED

    Enterprise SANs transitioning to SDS

    Advantages of SDS:

    Easy to take advantage of latest hardware features

    Easy to change hardware generation without changing operations

    External storage configurations changing to AFA

    Hybrid will continue to be a significant part of the overall market

    Market Trends

    All-flash arrays (AFAs)

    Hybrid flash arrays (HFAs)

    HDD only

    Source: IDC, Oct. 2016

    4

  • But... CPU/DRAM is the New BottleneckCPU/DRAM not keeping

    pace with storage/network

    What to do?

    Throw more CPU/DRAM at the problem?

    Can work

    But adds cost

    Make the CPU/DRAM more efficient

    SPDK

    User space

    Polling

    Zero Copy?

    Copyright 2017 FUJITSU LIMITED

    $

    5

  • SPDK is More Efficient

    Copyright 2017 FUJITSU LIMITED

    Linear scaling for 4 NVMe devices on 1 SPDK core for 4K random reads

    0

    200000

    400000

    600000

    800000

    1000000

    1200000

    1400000

    1600000

    1800000

    2000000

    1 2 4 8 16 32 64 128 256 512 1024

    IOP

    S

    Queue Depth

    1 DEV 2 DEV 4 DEV Kernel

    6

  • Target Market

    Use Cases

    Mission critical, high availability

    DB/OLTP acceleration

    Business decision acceleration

    Virtualized server infrastructure and VDI

    Goals:

    Ultimate performance

    Best in class IOPS/latency

    Cost efficient

    NVMe best in class $/IOP

    Further cost reduction via inexpensive SSDs or hybrid configurations

    Copyright 2017 FUJITSU LIMITED7

  • Copyright 2017 FUJITSU LIMITED

    Multiple Hardware Configurations on Same SPDK-based software

    AFA Configuration

    More performance

    SSDs & Nearline HDDsHybrid Configuration

    Comparable price withHDD systems

    Scalability

    Scale out & Scale up

    Connectivity

    Frontend: FC, iSCSI and NVM over Fabric

    Backend: NVMe & SAS

    Interconnect:Omni-Path, etc.

    NVMeSSD

    Linux/SPDK

    Scale out

    Scale Up

    Scale out

    Node 0

    Node 0

    Various Configurations by Common Architecture

    NVMeSSD

    Linux/SPDK

    Node n

    Linux/SPDK

    SASSSD

    NL HDD

    Node n

    Linux/SPDK

    SASSSD

    NL HDD

    8

  • How Are We Using SPDK

    Copyright 2017 FUJITSU LIMITED

    Now

    Future Interest

    Drivers

    Storage

    Services

    Storage

    Protocols

    iSCSI

    Target

    NVMe-oF*

    Target

    SCSI

    vhost-scsi

    Target

    NVMe

    NVMe Devices

    Blobstore

    NVMe-oF*

    Initiator

    Intel QuickData

    Technology Driver

    Block Device Abstraction (BDEV)

    Ceph

    RBD

    Linux

    Async IO

    Blob

    bdevFujitsu

    NVMe

    NVMe* PCIe

    Driver

    Released

    Q217

    Pathfinding

    vhost-blk

    TargetObject

    BlobFS

    Integration

    RocksDB

    Ceph

    9

  • Thread Model and DPDK

    Minimal dependence on DPDK

    Currently rely on rte_timer

    Copyright 2017 FUJITSU LIMITED

    SSD

    SSD

    SSD

    SSD

    SSD

    SSD

    SSD

    SSD

    SSD

    SSD

    SSD

    SSD

    Storage Services

    +

    Inter-Node Comm.

    Host I/F

    Host I/F

    Dedicated threads

    Thread pool

    bdev channels

    10

  • What Are We Interested In

    Bdev

    Optimize for NVMe

    Need ability to support for SAS/SATA

    Robust Error Recovery

    Timeouts

    Aborts

    Monitoring

    Performance

    Health

    Hot-plug

    User space TCP

    Future

    Vhost and NVMeF (target)

    Copyright 2017 FUJITSU LIMITED

    Enterprise

    11

  • Contributions

    SPDK community contributions

    30+ pull requests

    20+ merges

    NVMe-to-SCSI error code translation

    Register callback in the event of a timeout

    Consistent naming

    Abort option in timeout callback

    Etc..

    Copyright 2017 FUJITSU LIMITED12

  • What are we working on now

    NVMe passthrough command

    IOCTL like interface to obtain Health/SMART log pages

    Performance Monitoring

    PCIe Hot-plug

    FW download

    Copyright 2017 FUJITSU LIMITED13

  • Conclusion

    SPDK is a compelling architecture for Enterprise Grade SDS solutions

    Copyright 2017 FUJITSU LIMITED14

  • Copyright 2017 FUJITSU LIMITED

    Thank you!

    Questions?

    15

  • Copyright 2017 FUJITSU LIMITED

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