virtual machine disk images introspection

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Virtual Machine Disk Images Introspection. and a bit more. Vasily Tarasov (SBU) Dean Hildebrand (IBM Almaden) Renu Tewari (IBM Almaden) Erez Zadok (SBU). File system and Storage Lab ( F S L ). Outline. How all that started The idea of introspection - PowerPoint PPT Presentation

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Virtual Machine Disk Images Introspection

Vasily Tarasov (SBU)

Dean Hildebrand (IBM Almaden)

Renu Tewari (IBM Almaden)

Erez Zadok (SBU)

File system and Storage Lab (FSL)

and a bit more...

Outline

• How all that started

• The idea of introspection

• A couple of results from a 1st prototype

• Future work

• Benchmarking, Filebench

Virtual Machines (VMs)- Computational resources consolidation- Flexible, efficient and scalable- Hardware support- Multiple solutions: VMWare, KVM, Xen, ...- Cloud-way of delivering services

Network Attached Storage (NAS)- Storage consolidation- Scalable, manageable and efficient- NFS/CIFS available on majority of Operating Systems- NAS sales jumped from $540M in 1998 to $5.1B in 2003 - IBM SONAS

Two important technologies

VM NAS

Two technologies…

Dean

VM NAS

…and they grow

Dean

VM NASIBM

SO

How do VM & NAS work together?Can we make them work better?

Typical Setup

VM 2-1

VM 2-2Virtual

MachinesHost

2

NFS

CLIE

NT

Storage 2-1

Storage 2-2

GPFSNode 1

Storage 3-1

Storage 3-2GPFSNode 3

GPFSNode 1

Storage 4-1

Storage 4-2GPFSNode 4VM 3-2

VirtualMachines

Host3

NFS

CLIE

NT

VM 3-1

NFSSERVER

2

VM 1-1

VM 1-2Virtual

MachinesHost

1

NFS

CLIE

NT

NFSSERVER

1

Storage 1-1

Storage 1-2GPFSNode 1

GPFSNode 2

VMWare, KVM, XEN, ...

Datapath DecomposedApplications

Virtual File System

On-Disk File System

Block Layer

Controller Driver

Controller Emulator

NFS Client

VM

Guest

NETWORK

Host

NFS Server

Virtual File System

On-Disk File System

Block Layer

Controller Driver

NA

S

CA RA

RA RM

CA RA RM

RM

CA RA

RM

RM

CA RA

RA RM

CA RA RM

RM

– CAchingCA

– Read-AheadRA

– Request Mangling and Scheduling

RM

Collecting traces: setup

1Gbps

VMWare ESX4NFS Server

VSCSI LayerTrace

Network Trace

Block LayerTrace

Within VMtrace

Rand/Seq Read Rand/Seq Write Various I/O sizes Multi-file workloads Multi-process workloads Meta-data intensive

Collecting traces: setup

VSCSI LayerTrace

Network Trace

Applications

Virtual File System

On-Disk File System

Block Layer

Controller Driver

Controller Emulator

NFS Client

VM

Guest

NETWORK

Host

NFS Server

Virtual File System

On-Disk File System

Block Layer

Controller Driver

NA

SBlock Layer

Trace

User-SpaceWorkload

Rand/Seq Read Rand/Seq Write Various I/O sizes Multi-file workloads Multi-process workloads Meta-data intensive

Some interesting results

I/O sizes change4MB

4KB

128KB

256KB

Applications

Virtual File System

On-Disk File System

Block Layer

Controller Driver

Controller Emulator

NFS Client

VM

Guest

NETWORK

Host

NFS Server

Virtual File System

On-Disk File System

Block Layer

Controller Driver

NA

S

1MB

32KB

WIOV’11 - Revisiting the Storage Stack in Virtualized NAS Environments

Meta-data Ops Data Ops

# stat /foo/bar

sys_stat(/foo/bar)

NFS_GETATTR(foobar_fh)

# stat /foo/bar

sys_stat(/foo/bar)

NFS_READ(dskimg_fh)

Non-VM case VM case Update attributes List directories Creation/deletion Lookup Access permissions Link/Symlink operations

NFS_WRITE(dskimg_fh)

Come up with an idea

READ(dskfh, offset, len)

Disk Image File

NFSServer

OffsetSize

What is located inthis region?

Ext, NTFS,UFS, ...

READ from: Inode Directory entry Data of specific file ...

Do intelligent things!

Prototype Results: Find

find

0

5

10

15

20

25

30

35

40

35

7

Non-optimizedOptimized

Ru n

ti me

(sec

)80% improvement

Prototype Results: Startup

Non-optimized Optimized0

20

40

60

80

100

120

140130 sec

50 sec

2.6x times faster

Future work

• Solid implementation

• More efficient cache policies

• Optimizations on the write path

• Analysis of more complex workloads

Virtual MachineDisk Images Introspection

a bit more...

18

A Recent Study Concluded that…1. Much of what researchers conclude in their studies is

misleading, exaggerated,or flat-out wrong

2. A new claim about a research findings is more likely to be false than true

3. Researchers tend to publish positive results more often than negative findings

4. Chances to be accepted to a conference are higher if the results are “more exciting”

5/4/2011

MedicineA SociologyB Computer ScienceC

BiologyD PhysicsE

2005-2008 studyby J. Ioannidis

HotOS’11: Benchmarking FS Benchmarking: It is Rocket Science

Filebench

• Originally created by SUN Microsystem (RIP )• Maintained by FSL• Used in many papers• Flexible: Workload Model Language – WML• Portable: Linux, FreeBSD, Solaris, MacOS,

Windows *

Filebench WML

define fileset name=myfileset,size=16kb,entries=1000define process name=reader,instances=1 {

thread name=readerthread,memsize=10m,instances=10 {

flowop read name=myread,filesetname=myfileset,iosize=2kb }

}

Filebench for Cloud Services

• Reads• Writes• Creates• Deletes• +20 more

sophisticated

POSIX

NFS RPC

AFS RPC

Cloud

flowops:

Filebench for Virtualized Environments

define process name=reader,instances=1 {

thread name=readerthread,memsize=10m,instances=10 {

flowop read name=myread1,filesetname=myfileset,…

}

}

define hypervisor name=hpv,type=esx3.1,instances=1{

define vm name=hpv,type=windows,instances=5{

}}

Virtual Machine Disk Images Introspection

Vasily Tarasov (SBU)

Dean Hildebrand (IBM Almaden)

Renu Tewari (IBM Almaden)

Erez Zadok (SBU)

File system and Storage Lab (FSL)

and a bit more...

Thank you!

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