vcacheshare:automatedserver* flashcachespace ... · vcacheshare (vcs) overview dynamic cache...

vCacheShare: Automated Server Flash Cache Space Management in

a Virtualiza;on Environment

Fei Meng Xiaosong Ma Li Zhou∗

Sandeep U3amchandani Deng Liu∗

∗With VMware during this work

Background

•  Virtualization  

Guest OS

Apps Apps Apps

Hypervisor

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Background

•  Virtualization •  Server Flash Cache

Guest OS

Apps Apps Apps

Hypervisor

(Server Flash Cache) SFC

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Background

•  Virtualization •  Server Flash Cache

Guest OS

Apps Apps Apps

Hypervisor

(Server Flash Cache) SFC

I/O acceleraNon

Increased VM-‐to-‐server consolidaNon raNos

Reduced disk I/O load

Reduced inter-‐server I/O contenNon

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Problem Setting and Motivations

•  SFC management  

 

   

 

Hypervisor SFC

VM5 VM1 VM2 VM3 VM4

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•  SFC management – Static space

partitioning

   

 

 

Hypervisor SFC

SFC1

VM5 VM1 VM2 VM3 VM4

SFC2 SFC3 SFC4 SFC5

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partitioning

– Globally shared  

 

 

Hypervisor SFC

VM5 VM1 VM2 VM3 VM4

Shared

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partitioning

– Globally shared •  Issues

– Different access behavior across VMs

– Temporal changes in locality

Hypervisor SFC

VM5 VM1 VM2 VM3 VM4

Shared

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partitioning

– Globally shared •  Issues

– Different access behavior across VMs

– Temporal changes in locality

Hypervisor SFC

VM5 VM1 VM2 VM3 VM4

SFC1 SFC2 SFC3 SFC4 SFC5

vCacheShare

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vCacheShare Design Goal

Automated and continuously optimized SFC space allocation, considering

         

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vCacheShare Design Goal

Automated and continuously optimized SFC space allocation, considering

– VM priority – Locality of workloads –  IO access characteristics – Backend device service time – Configuration events (e.g., migration)

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vCacheShare (vCS) Overview

Dynamic cache partitioning based on – Continuous per-VM access history collection

and analysis – Comprehensive cache utility model

•  “How useful is more cache space to this workload?” •  Combining hit ratio and reuse intensity in cache

utility calculation –  Long-term vs. short-term observation – Different turnaround requirement for access history

analysis

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vCacheShare Design

SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS

Cache management(bookkeeping, hash, LRU

list management etc.)

I/O

Monitor

trace buffersI/O log

ActuatorResize actions

Recommended cache allocation plan

Cache utility analyzer

I/O logsRD

Optimizerlinear, non-

linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM

Cluster management node Database

Administration eventssuch as VMotion

RD array

Cluster

vmdk

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vCacheShare Design

SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

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ESXi

vCS Component 1: Cache Module

SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

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Cache module

 


SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

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VM

SFC

Disk array

Cache module

Cache policy illustraNon

write

read

read

• Write-around read cache


SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

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•  Block-level cache targeting hypervisor    

 

VM

SFC

Disk array

Cache module


write

read

read



SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

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•  Block-level cache targeting hypervisor –  Dynamically resizable  

 

VM

SFC

Disk array

Cache module


write

read

read



SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

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•  Block-level cache targeting hypervisor –  Dynamically resizable –  VM-aware

 

VM

SFC

Disk array

Cache module


write

read

read



SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

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•  Block-level cache targeting hypervisor –  Dynamically resizable –  VM-aware

•  LRU

VM

SFC

Disk array

Cache module


write

read

read


vCS Component 2: Monitor

SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

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vCS monitor


SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

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•  Location: In I/O path, between VMs and SFC  

   

   

vCS monitor


SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

struct cache_io_stats { unit16 VM_UUID; unit16 VMDK_UUID; unit16 Nmestamp; uint8 isRead; uint32 LBA; uint8 len; uint16 latency; uint8 isCached;

}

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•  Location: In I/O path, between VMs and SFC •  Intercepts I/O accesses for trace collection

–  In-memory plus on-SSD circular buffer –  Copy segments (sliding window) to Analyzer’s buffers

   

vCS monitor

Cache stat entry

Time window

*Analyzer *SSD buffer Copy to

In-‐memory buffer


SSD

Intercept

HypervisorVMKernel

User world

SAN, NAS



I/O

Monitor





I/O logsRD


linear solver etc

CUsvCacheShare

Cache module

Hardware

I/O I/O

vmdk

VMVMVM



RD array

Cluster

vmdk

struct cache_io_stats { unit16 VM_UUID; unit16 VMDK_UUID; unit16 Nmestamp; uint8 isRead; uint32 LBA; uint8 len; uint16 latency; uint8 isCached;

}

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•  Location: In I/O path, between VMs and SFC •  Intercepts I/O accesses for trace collection

–  In-memory plus on-SSD circular buffer –  Copy segments (sliding window) to Analyzer’s buffers

•  Overhead tolerable for workloads tested –  Sampling possible if necessary

vCS monitor

Cache stat entry

Time window

*Analyzer *SSD buffer Copy to

In-‐memory buffer

vCS Component 3: Analyzer

•  Processes trace segments from Monitor •  Output

   

     

     

 

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VM 1:

VM 2:



– Reuse distance CDF •  Used in calculating Hit Ratio (HR)

– Measuring long-term locality behavior – Commonly used in cache partitioning [Mattson1970][Ding2003]

–  But incomplete: does not capture access speed

     

 

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VM 1:

VM 2:






     

 

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VM1: … 1 2 7 8

VM2: … 1 2 7 8

OpNmizaNon Nme

VM 1:

VM 2:

2 hours

…

…

10 seconds






– Reuse Intensity (RI) •  Measuring how fast are data re-visited •  Estimated by ratio of total accessed volume and

footprint •  Coarse and more frequent measurement

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)/( uniquewtotal StS ×

VM 1:

VM 2:

vCS Component 4: Optimizer

•  For each VM – Cache utility

   

 

 

CUi (c) = li ×RRi × (HRi (c)+αRIi )

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Backend device latency

Read raNo

EsNmated hit raNo

Reuse intensity

Tuning knob



•  Optimization – Objective function

max

– Constraints


priorityi ×CUii=1

n

∑ (c)

Cccc n =+++ !21

maxmin ccc i ≤≤

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Read raNo

EsNmated hit raNo

Reuse intensity

Tuning knob



•  Optimization – Objective function

max

– Constraints <c1, c2, c3, …> Adjusted cache size


priorityi ×CUii=1

n

∑ (c)

Cccc n =+++ !21

maxmin ccc i ≤≤

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Read raNo

EsNmated hit raNo

Reuse intensity

Simulated annealing

Tuning knob

vCacheShare ESX Event Handling

VM Events AcNons by vCacheShare Framework

VM Power-‐Off, Suspend, vMoNon Source

Trigger opNmizaNon to re-‐allocate; free IO trace buffers for all associated VMDKs

VM Bootstrap, Power-‐On, Resume

Make iniNal allocaNon based on reserved cache space and priority seings; start trace collecNon

vMoNon DesNnaNon

Trigger opNmizaNon to re-‐allocate based on IO characterisNcs migrated with the VMDKs involved in vMoNon

Storage vMoNon (runNme backend de-‐ vice change)

Suspend analysis/opNmizaNon Nll compleNon; evict device service latency history; trigger re-‐ opNmizaNon upon vMoNon compleNon

VM Fast Suspend, vMoNon Stun

Reserve cached data; lock cache space allocaNon to involved VMs by subtracNng allocated size from total available cache size

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Evaluation Setup

•  Prototype – VMware EXSi 5.0 with ~2800 LOC C++ user

space code and ~2500 LOC kernel C code •  Hardware

– Two AMD Opteron 8-core CPU, 16GiB memory, Intel 400GiB SSD

– EMC Clariion array •  VM configuration

– 1vCPU, 1GiB memory and 8/80GiB VMDK – Win2k8, Ubuntu 11.04

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0

20

40

60

80

100

150 300 450 600 750 900 1050 1200 1350 1500

Cac

he a

lloca

tion

(%)

Time (s)

VM1 GLRUVM2 GLRU

VM1 vCSVM2 vCS

Micro-Benchmark: Iometer

VM1 footprint shrunk

VM2 replay

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0

20

40

60

80

100

150 300 450 600 750 900 1050 1200 1350 1500

Cac

he a

lloca

tion

(%)

Time (s)

VM1 GLRUVM2 GLRU

VM1 vCSVM2 vCS

Micro-Benchmark: Iometer

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1

2

3

4

5

6

150 300 450 600 750 900 1050 1200 1350

IO la

tenc

y (m

s)

Time (s)

VM1 GLRU VM2 GLRU VM1 vCS VM2 vCS

Cache allocaNon

Workload latency

Macro-benchmark: VDI 100 VDI VMs going through boot storm + 1 Iometer VM

0

100

200

300

400

500

0 50 100 150 200 250 300 350

Late

ncy

(ms)

Time (s)

GLRU vCS Static No cache

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StaNc: 72s vCS: 114s

GLRU:242s No cache:298s

Macro-benchmark: VDI 100 VDI VMs going through boot storm + 1 Iometer VM

0

100

200

300

400

500

0 50 100 150 200 250 300 350

Late

ncy

(ms)

Time (s)

GLRU vCS Static No cache

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0

1

2

3

4

5

20 40 60 80 100 120 140 160

Late

ncy

(ms)

Time (s)

vCS VDI Static VDI

VDI latency

Iometer latency

Closing Remarks

•  Dynamic SFC partitioning affordable and worthwhile

•  Long sampling windows desirable for accurate cache hit ratio estimation –  But cause slow response to locality spikes –  Can be compensated by simultaneous short-term

locality monitoring •  Future work: relationship between sampling

window size and cache hit ratio estimation accuracy

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Q&A

Fei Meng [email protected]

North Carolina State University

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References

•  [Mattson1970] R. L. Mattson, J. Gecsei, D. R. Slutz, and I. L. Traiger. Evaluation techniques for storage hierarchies. IBM Syst. J., 9(2):78–117, June 1970.

•  [Ding2003] C. Ding and Y. Zhong. Predicting Whole-program Locality through Reuse Distance Analysis. In ACM SIGPLAN Notices 2003.

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