computer architecture and memory systems lab

Computer Architecture and MEmory systems Lab.

Understanding System Characteristics of Online Erasure Coding on Scalable, Distributed and

Large-Scale SSD Array Systems

Sungjoon Koh, Jie Zhang, Miryeong Kwon, Jungyeon Yoon, David Donofrio, Nam Sung Kim and Myoungsoo Jung

Take-away

• Motivation: Distributed systems are starting to adopt erasure coding as a fault tolerance mechanism instead of replication due to its storage overheads.

Take-away


• Goal: Understanding system characteristics of online erasure coding by analyzing and comparing them with those of replication.

Take-away



• Observations in Online Erasure Coding:1) Up to 13× I/O performance degradation compared to replication.2) 50% CPU usage and lots of context switches.3) Up to 700× I/O amplification more than total request volumes.4) Up to 500× network traffics among the storage nodes compared to total

request amount.

Take-away



• Observations in Online Erasure Coding:1) Up to 13× I/O performance degradation compared to replication.2) 50% CPU usage and lots of context switches.3) Up to 700× I/O amplification more than total request volumes.4) Up to 500× network traffics among the storage nodes compared to total

request amount.

• Summary of Our Work:• We observe and measure various overheads imposed by online erasure

coding quantitatively on a distributed system that consists of 52 SSDs.• Collect block-level traces from all-flash array based storage clusters, which

can be downloaded freely.

Overall Results

Read Write Read Write Read Write Read Write Read Write Read Write

Throughput LatencyCPU

Utilization

Relative

Context

Switch

Private

Network

Overhead

I/O

Amplification

05

10253035505560

57.7

10.4

37.8

8.712.6

1.9

10.77.6

1.50.14

4KB Random Request

3-Replication (3-Rep.)

RS (10, 4)

No

rmalized

to

Re

plicati

on

0.67

Almost 0

Introduction

Demands on scalable, high performancedistributed storage system

Introduction

Employing SSDs to HPC & DC Systems

HPC & DC

SSD


HPC & DC

SSDHDD


HPC & DC

SSDHDDHigher bandwidthShorter latency &

Lower power consumption


HPC & DC

• Typically, storage systems have regular failures.

Storage System Failures


1) Storage failure

ex) Facebook reports; Up to 3% HDDs fails each day(Ref. M. Sathiamoorthy et al., “Xoringelephants: Novel erasure codes for big data,” in PVLDB, 2013.)



1) Storage failure

Although SSDs have higher reliability than HDDs, daily failure cannot be ignored.




1) Storage failure


2) Network switch errors, power outages, and soft/hard errors




1) Storage failure


2) Network switch errors, power outages, and soft/hard errors


“So we need fault tolerance mechanism.”


Traditional fault tolerance mechanism.

Replication

Data Replica Replica

Fault Tolerance Mechanisms in Distributed System


• Simple and effective way to make system resilient.

Replication





× High storage overheads (3x).

Replication






× Especially for SSD, replication

Replication







1) causes high expense because of SSD’s high cost per GB.

Replication








2) causes performance degradation due to SSD’s specific characteristics .

ex) Garbage collection, wearing out…

Replication








2) causes performance degradation due to SSD’s specific characteristics .

ex) Garbage collection, wearing out…

Replication



Need an alternative method to reduce the storage overheads.

Alternative method of replication.

Erasure coding


Data Chunks Coding Chunks

Encode


• Lower storage overheads than replication.

Erasure coding



Encode



× High reconstruction costs. (well known problem)

Erasure coding



Encode




ex) Facebook cluster with EC increases network traffics by more than 100TB in a

day.

Erasure coding



Encode





day.

Many researches try to reduce reconstruction costs.

Erasure coding



Encode





day.

Many researches try to reduce reconstruction costs.

Erasure coding



Encode

We observed significant overheadsimposed during I/O services in distributed

system employing erasure codes.

• Reed-Solomon: Erasure coding algorithm.

• Ceph: Distributed system used in this research.

- Architecture

- Data path

- Storage stack

Background

• The most famous erasure coding algorithm.

Reed-Solomon


• Divide data into k equal data chunks and generates m coding chunks.

Reed-Solomon

D0

D1...

Dk

Data

C0

C1...

CmData

ChunksCodingChunks



• Encoding: Multiplication of a generator matrix and data chunks as a vector.

Reed-Solomon

DataChunks

D0

D1

D2

D3

D0

D1

D2

D3

C0

C1

GeneratorMatrix

C2

CodingChunks




• Stripe: k data chunks.

Reed-Solomon

DataChunks

D0

D1

D2

D3

D0

D1

D2

D3

C0

C1

GeneratorMatrix

C2

Stripe





• Write Reed-Solomon with k data chunks and m coding chunks as “RS(k,m)”.

Reed-Solomon

DataChunks

D0

D1

D2

D3

D0

D1

D2

D3

C0

C1

GeneratorMatrix

C2

RS(4,3)





• Write Reed-Solomon with k data chunks and m coding chunks as “RS(k,m)”.

• Can be recovered from m failures.

Reed-Solomon

DataChunks

D0

D1

D2

D3

D0

D1

D2

D3

C0

C1

GeneratorMatrix

C2

3 failures

App

libRBD

libRADOS

App App

libRBD

libRADOS

App

Client Nodes

Public Network

Node 0 Node n

Monitor Monitor

PrivateNetwork

Storage

Ceph Architecture

App

libRBD

libRADOS

App App

libRBD

libRADOS

App

Client Nodes

Public Network

Node 0 Node n

Monitor Monitor

PrivateNetwork

Storage

Ceph Architecture

• Client nodes are connected to the storage nodes through “public network”.

App

libRBD

libRADOS

App App

libRBD

libRADOS

App

Client Nodes

Public Network

Node 0 Node n

Monitor Monitor

PrivateNetwork

Storage

Ceph Architecture


• Storage nodes are connected through “private network”.

App

libRBD

libRADOS

App App

libRBD

libRADOS

App

Client Nodes

Public Network

Node 0 Node n

Monitor Monitor

PrivateNetwork

Storage

Ceph Architecture



• Each storage node consists of several object storage device daemons (OSDs) and monitors.

• OSDs handle read/write services.

App

libRBD

libRADOS

App App

libRBD

libRADOS

App

Client Nodes

Public Network

Node 0 Node n

Monitor Monitor

PrivateNetwork

Storage

Ceph Architecture






App

libRBD

libRADOS

App App

libRBD

libRADOS

App

Client Nodes

Public Network

Node 0 Node n

Monitor Monitor

PrivateNetwork

Storage

Ceph Architecture





• Monitors manage the access permissions and the status of multiple OSDs.

Data Path

file

Object libRBDObject

1. File/block is handled as an object.

Data Path

file

Object libRBD

libRADOS

Pool APool Boid

HASH()pgid

Object


2. Object is assigned to placement group

(PG) consists of several OSDs according

to the result of hash function.

01

n

17,18,19,11

PG

Data Path

file

Object libRBD

libRADOS

Pool APool Boid

HASH()pgid

CRUSH()

Object





3. CRUSH algorithm determines primary

OSD in PG.

01

n

17,18,19,11

PG

Data Path

file

Object libRBD

libRADOS

Pool APool Boid

HASH()pgid

CRUSH()

primaryObject






OSD in PG.

4. Object is sent to primary OSD.01

n

17,18,19,11

PG

Data Path

file

Object libRBD

libRADOS

Pool APool Boid

HASH()pgid

CRUSH()

primaryObjectData 1 Data 2 Data 3 Data 4






OSD in PG.

4. Object is sent to primary OSD.

5. Primary OSD sends object to another

OSDs (secondary, tertiary, …) as a form

of replica/chunk depending on the fault

tolerance mechanism.

01

n

17,18,19,11

PG

Data Path

file

Object libRBD

libRADOS

Pool APool Boid

HASH()pgid

CRUSH()

primaryObjectData 1 Data 2 Data 3 Data 4






OSD in PG.

4. Object is sent to primary OSD.

5. Primary OSD sends object to another

OSDs (secondary, tertiary, …) as a form

of replica/chunk depending on the fault

tolerance mechanism.

“In detail”

01

n

17,18,19,11

PG

Storage Stack

Primary OSDObject

Storage Stack

Primary OSD

Client Messenger

Object

Storage Stack

Primary OSD

Client Messenger

Dispatcher

Object

Storage Stack

Primary OSD

Client Messenger

Dispatcher

PrimaryLogPG

Object

Storage Stack

Primary OSD

Client Messenger

Dispatcher

PrimaryLogPG

PG BackendReplicated EC

Object

Storage Stack

Primary OSD

Client Messenger

Dispatcher

PrimaryLogPG


Cluster Messenger

Object

Chunk/Replica

Storage Stack

Primary OSD

Client Messenger

Dispatcher

PrimaryLogPG


Dispatcher

PrimaryLogPG


SSD SSD

Cluster Messenger

Object

Chunk/Replica

Storage Stack

Primary OSD

Client Messenger

Dispatcher

PrimaryLogPG


Dispatcher

PrimaryLogPG


SSD SSD

Cluster Messenger

Object

“Implemented in user space.”

User space

Kernel space

Chunk/Replica

Client

Core Core Core Core24

SSD SSD6TB

256GB DDR4 DRAM

SSDSSD

Mon

OSD1

OSD6

Mon

OSD1

OSD6

Mon

OSD1

OSD6

OSD1

OSD6

10Gb

10Gb

10Gb

Ceph Storage Cluster

PublicNetwork

PrivateNetwork

Analysis Overview

Client


SSD SSD6TB

256GB DDR4 DRAM

SSDSSD

Mon

OSD1

OSD6

Mon

OSD1

OSD6

Mon

OSD1

OSD6

OSD1

OSD6

10Gb

10Gb

10Gb


PublicNetwork

PrivateNetwork

Analysis Overview

1) Overall performance.- throughput & latency

Client


SSD SSD6TB

256GB DDR4 DRAM

SSDSSD

Mon

OSD1

OSD6

Mon

OSD1

OSD6

Mon

OSD1

OSD6

OSD1

OSD6

10Gb

10Gb

10Gb


PublicNetwork

PrivateNetwork

Analysis Overview


2) CPU utilization & # context switches.

Client


SSD SSD6TB

256GB DDR4 DRAM

SSDSSD

Mon

OSD1

OSD6

Mon

OSD1

OSD6

Mon

OSD1

OSD6

OSD1

OSD6

10Gb

10Gb

10Gb


PublicNetwork

PrivateNetwork

Analysis Overview



3) Actual amount of reads & writes served from disks.

Client


SSD SSD6TB

256GB DDR4 DRAM

SSDSSD

Mon

OSD1

OSD6

Mon

OSD1

OSD6

Mon

OSD1

OSD6

OSD1

OSD6

10Gb

10Gb

10Gb


PublicNetwork

PrivateNetwork

Analysis Overview




4) Private network traffics.

Object Management in Erasure Coding

Observe that erasure coding has different object management

scheme with replication.




- To manage data/coding chunks.





- Two phases: object initialization & object update.






i) Object initialization.

<4MB Object>

𝑘KB Write







<4MB Object>

𝑘KB Write

Dummy Data







<4MB Object>

𝑘KB Write

Dummy DataGenerate object with

coding chunks


ii) Object update.

Data Chunk 2

To be updated


ii) Object update.

Data Chunk 1

Data Chunk 2

Data Chunk 3

Data Chunk 4

Data Chunk 5

Data Chunk 0

i) Read whole stripe

To be updated


ii) Object update.

Data Chunk 1

Data Chunk 2

Data Chunk 3

Data Chunk 4

Data Chunk 5

Data Chunk 0

To be updated

ii) Generate coding chunks

Coding Chunk 0

Coding Chunk 1

Coding Chunk 2


ii) Object update.

Data Chunk 1

Data Chunk 2

Data Chunk 3

Data Chunk 4

Data Chunk 5

Data Chunk 0

To be updated

Coding Chunk 0

Coding Chunk 1

Coding Chunk 2

iii) Write to storage

Workload Description

3-replication, RS(6,3), RS(10,4)

Workload Description

Micro benchmark: Flexible I/O (FIO)

Request Size (KB) 1, 2, 4, 8, 16, 32, 64, 128

Access Type Sequential Random

Pre-write X O X O

Operation Type Write Read Wrie Write Read Write

3-replication, RS(6,3), RS(10,4)

Client


SSD SSD6TB

256GB DDR4 DRAM

SSDSSD

Mon

OSD1

OSD6

Mon

OSD1

OSD6

Mon

OSD1

OSD6

OSD1

OSD6

10Gb

10Gb

10Gb


PublicNetwork

PrivateNetwork

Analysis Overview





Performance Comparison (Sequential Write)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

0

4

8

124KB

2KB1KB

3-Rep. RS 6, 3 RS 10, 4

Th

rou

gh

pu

t (M

B/s

)

- worse in RS (max)

- longer in RS (max)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

1000

1200 3-Rep.

RS 6, 3

RS 10, 4

La

ten

cy

(m

se

c)


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

0

4

8

124KB

2KB1KB

3-Rep. RS 6, 3 RS 10, 4

Th

rou

gh

pu

t (M

B/s

)

- Significant performance degradation in Reed-Solomon.

- worse in RS (max)


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

1000

1200 3-Rep.

RS 6, 3

RS 10, 4

La

ten

cy

(m

se

c)


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

0

4

8

124KB

2KB1KB

3-Rep. RS 6, 3 RS 10, 4

Th

rou

gh

pu

t (M

B/s

)

X 11.3 (MAX)

- worse in RS (max)


- Throughput: 11.3× worse in RS (max)


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

1000

1200 3-Rep.

RS 6, 3

RS 10, 4

La

ten

cy

(m

se

c)


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

0

4

8

124KB

2KB1KB

3-Rep. RS 6, 3 RS 10, 4

Th

rou

gh

pu

t (M

B/s

)

X 11.3 (MAX)


- worse in RS (max)


- Latency: 12.9× longer in RS (max)


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

1000

1200 3-Rep.

RS 6, 3

RS 10, 4

La

ten

cy

(m

se

c)

X 12.9 (MAX)


4KB8KB

16KB0

20

40

60 3-Rep.

RS 6, 3

RS 10, 4

Th

rou

gh

pu

t (M

B/s

)


- worse in RS (max)


- Degradation in request size 4~16KB is not acceptable


4KB8KB

16KB0

200

400

600

800

1000

1200

1400 3-Rep. RS 6, 3 RS 10, 4

La

ten

cy

(m

se

c)


4KB8KB

16KB0

20

40

60 3-Rep.

RS 6, 3

RS 10, 4

Th

rou

gh

pu

t (M

B/s

)


- worse in RS (max)


- Degradation in request size 4~16KB is not acceptable

- Computation for encoding, data management, and additional network traffic

causes degradation in erasure coding.

4KB8KB

16KB0

200

400

600

800

1000

1200

1400 3-Rep. RS 6, 3 RS 10, 4

La

ten

cy

(m

se

c)

Performance Comparison (Sequential Read)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

1000

0

8

16

24

324KB

2KB

1KB

Th

rou

gh

pu

t (M

B/s

) 3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

50

100

150 3-Rep. RS 6, 3 RS 10, 4

La

ten

cy

(m

se

c)

- worse in RS (4KB)

- longer in RS (4KB)


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

1000

0

8

16

24

324KB

2KB

1KB

Th

rou

gh

pu

t (M

B/s

) 3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

50

100

150 3-Rep. RS 6, 3 RS 10, 4

La

ten

cy

(m

se

c)

- Performance degradation in Reed-Solomon.

- worse in RS (4KB)



1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

1000

0

8

16

24

324KB

2KB

1KB

Th

rou

gh

pu

t (M

B/s

) 3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

50

100

150 3-Rep. RS 6, 3 RS 10, 4

La

ten

cy

(m

se

c)

- worse in RS (4KB)


- Throughput: 3.4× worse in RS (4KB)


X 3.4


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

1000

0

8

16

24

324KB

2KB

1KB

Th

rou

gh

pu

t (M

B/s

) 3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

50

100

150 3-Rep. RS 6, 3 RS 10, 4

La

ten

cy

(m

se

c)


- worse in RS (4KB)


- Latency: 3.4× longer in RS (4KB)


X 3.4 X 3.4


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

1000

0

8

16

24

324KB

2KB

1KB

Th

rou

gh

pu

t (M

B/s

) 3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

50

100

150 3-Rep. RS 6, 3 RS 10, 4

La

ten

cy

(m

se

c)


- worse in RS (4KB)


- Even though there was no failure, performance degradation occurred.



1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

200

400

600

800

1000

0

8

16

24

324KB

2KB

1KB

Th

rou

gh

pu

t (M

B/s

) 3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

50

100

150 3-Rep. RS 6, 3 RS 10, 4

La

ten

cy

(m

se

c)


- worse in RS (4KB)



- Caused by RS-concatenation, which generates extra data transfers.



- worse in RS (4KB)




Data Chunk 1

Data Chunk 2

Data Chunk 3

Data Chunk 4

Data Chunk 5

Data Chunk 0

“RS-Concatenation”



- worse in RS (4KB)




Data Chunk 1

Data Chunk 2

Data Chunk 3

Data Chunk 4

Data Chunk 5

Data Chunk 0

“RS-Concatenation”

Stripe

Client


SSD SSD6TB

256GB DDR4 DRAM

SSDSSD

Mon

OSD1

OSD6

Mon

OSD1

OSD6

Mon

OSD1

OSD6

OSD1

OSD6

10Gb

10Gb

10Gb


PublicNetwork

PrivateNetwork

Analysis Overview





Computing and Software Overheads (CPU Utilization)

<Random Read><Random Write>



- RS requires much more CPU cycles than replication.




- User mode CPU utilizations account for 70~75% of total CPU cycles.

70~75%





- Uncommon in RAID systems

70~75%





- Uncommon in RAID systems

- Implemented at the user level. (ex. OSD daemon, PG backend, fault tolerant modules)

70~75%

Computing and software overheads (Context Switch)


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

80k

160k

240k

0

3k

6k

9k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

500k

1M

0

20k

40k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4

elative number of context switches =The number of context switches

Total amount of request (MB)



1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

80k

160k

240k

0

3k

6k

9k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

500k

1M

0

20k

40k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4

- The number of context switches Total amount of request MB

- )𝐵𝑀( 𝑡𝑠𝑒𝑢𝑞𝑒𝑟 𝑓𝑜 𝑡𝑛𝑢𝑜𝑚𝑎 𝑙𝑎𝑡𝑜 The number of context switches Total amount of request

sehctiws txetnoc fo rebmun evitaleRelative number of context switches =

The number of context switches

Total amount of request (MB)



1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

80k

160k

240k

0

3k

6k

9k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

500k

1M

0

20k

40k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4


- Much more context switches occur in RS than replication.



1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

80k

160k

240k

0

3k

6k

9k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

500k

1M

0

20k

40k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4



i) Read: Data transfers through OSDs and computations during RS-concatenation.



1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

80k

160k

240k

0

3k

6k

9k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

500k

1M

0

20k

40k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4




ii) Write:



1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

80k

160k

240k

0

3k

6k

9k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

500k

1M

0

20k

40k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4




ii) Write:

1) Initializing object has lots of writes, and significant amount of computations.



1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

80k

160k

240k

0

3k

6k

9k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

500k

1M

0

20k

40k 32KB

64KB

128KB

Co

nte

xt

Sw

itc

h (

#/M

B)

3-Rep. RS 6, 3 RS 10, 4




ii) Write:

1) Initializing object has lots of writes, and significant amount of computations.

2) Updating object introduces many transfers among OSDs through user-level modules.

Client


SSD SSD6TB

256GB DDR4 DRAM

SSDSSD

Mon

OSD1

OSD6

Mon

OSD1

OSD6

Mon

OSD1

OSD6

OSD1

OSD6

10Gb

10Gb

10Gb


PublicNetwork

PrivateNetwork

Analysis Overview





I/O Amplification (Random Write)

/O amplification =Read/write amount from storage(MB)

Total amount of request(MB)

<Read Amplification><Write Amplification>

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0100200300400500600700

32 64 1280

8

16

24

3-Rep. RS 6, 3 RS 10, 4

Wri

te t

o S

tora

ge (

MB

)

To

tal R

eq

uest

(MB

)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

10

20

30

40

50

32 64 1280.0

0.8

1.6

2.4

3-Rep. RS 6, 3 RS 10, 4

Read

fro

m S

tora

ge (

MB

)

To

tal R

eq

uest

(MB

)


i)/O amplification= Read/write amount from storage(MB) Total amount of

request(MB) Read/write amount from storage(MB) Read/write amount from

storage(MB) Total amount of request(MB) Total amount of request(MB)

Read/write amount from storage(MB) Total amount of request(MB)

ii) I/O amplification =Read/write amount from storage(MB)

Total amount of request(MB)


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0100200300400500600700

32 64 1280

8

16

24

3-Rep. RS 6, 3 RS 10, 4

Wri

te t

o S

tora

ge (

MB

)

To

tal R

eq

uest

(MB

)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

10

20

30

40

50

32 64 1280.0

0.8

1.6

2.4

3-Rep. RS 6, 3 RS 10, 4

Read

fro

m S

tora

ge (

MB

)

To

tal R

eq

uest

(MB

)






ii) Erasure coding causes write amplification up to 700 times more than total

request volume.


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0100200300400500600700

32 64 1280

8

16

24

3-Rep. RS 6, 3 RS 10, 4

Wri

te t

o S

tora

ge (

MB

)

To

tal R

eq

uest

(MB

)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

10

20

30

40

50

32 64 1280.0

0.8

1.6

2.4

3-Rep. RS 6, 3 RS 10, 4

Read

fro

m S

tora

ge (

MB

)

To

tal R

eq

uest

(MB

)






ii) Erasure coding causes write amplification up to 700 times more than total

request volume.

iii) Why is write amplification by random writes so big?


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0100200300400500600700

32 64 1280

8

16

24

3-Rep. RS 6, 3 RS 10, 4

Wri

te t

o S

tora

ge (

MB

)

To

tal R

eq

uest

(MB

)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

10

20

30

40

50

32 64 1280.0

0.8

1.6

2.4

3-Rep. RS 6, 3 RS 10, 4

Read

fro

m S

tora

ge (

MB

)

To

tal R

eq

uest

(MB

)

I/O Amplification (Read)

<Sequential Read><Random Read>

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

10

20

30

40

32 64 1280.8

1.2

1.6

2.0

2.4

3-Rep. RS 6, 3 RS 10, 4

Read

fro

m S

tora

ge

(M

B)

To

tal R

eq

uest

(MB

)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0.6

0.8

1.0

1.2

1.4

Read

fro

m S

tora

ge (

MB

)

To

tal R

eq

ues

t (M

B) 3-Rep. RS 6, 3 RS 10, 4


- Read amplification caused by RS-concatenation.


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

10

20

30

40

32 64 1280.8

1.2

1.6

2.0

2.4

3-Rep. RS 6, 3 RS 10, 4

Read

fro

m S

tora

ge

(M

B)

To

tal R

eq

uest

(MB

)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0.6

0.8

1.0

1.2

1.4

Read

fro

m S

tora

ge (

MB

)

To

tal R

eq

ues

t (M

B) 3-Rep. RS 6, 3 RS 10, 4



i) Random read: Mostly reads different stripes. Lots of read amplifications.


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

10

20

30

40

32 64 1280.8

1.2

1.6

2.0

2.4

3-Rep. RS 6, 3 RS 10, 4

Read

fro

m S

tora

ge

(M

B)

To

tal R

eq

uest

(MB

)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0.6

0.8

1.0

1.2

1.4

Read

fro

m S

tora

ge (

MB

)

To

tal R

eq

ues

t (M

B) 3-Rep. RS 6, 3 RS 10, 4



i) Random read: Mostly reads different stripes. Lots of read amplifications.

ii) Sequential read: Consecutive I/O requests read data from same stripe. no read

amplifications.


1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

10

20

30

40

32 64 1280.8

1.2

1.6

2.0

2.4

3-Rep. RS 6, 3 RS 10, 4

Read

fro

m S

tora

ge

(M

B)

To

tal R

eq

uest

(MB

)

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0.6

0.8

1.0

1.2

1.4

Read

fro

m S

tora

ge (

MB

)

To

tal R

eq

ues

t (M

B) 3-Rep. RS 6, 3 RS 10, 4

Client


SSD SSD6TB

256GB DDR4 DRAM

SSDSSD

Mon

OSD1

OSD6

Mon

OSD1

OSD6

Mon

OSD1

OSD6

OSD1

OSD6

10Gb

10Gb

10Gb


PublicNetwork

PrivateNetwork

Analysis Overview





Network Traffics Among the Storage Nodes

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

100

200

300

400

500

0

5

10

15

20 32KB

64KB

128KB

Pri

va

te N

etw

ork

Tra

ffic

(M

B)

To

tal

Re

qu

es

t (M

B) 3-Rep. RS 6, 3 RS 10, 4

1KB2KB

4KB8KB

16KB32KB

64KB128KB

0

2

4

6

8

10

Pri

va

te N

etw

ork

Tra

ffic

(M

B)

To

tal

Re

qu

es

t (M

B) 3-Rep.

RS 6, 3

RS 10, 4


- Show similar trend with I/O amplifications.

- Erasure coding

i) Write: initializing & updating objects in erasure coding cause lots of network traffics.

ii) Read: RS-concatenation cause lots of network traffics.

- Replication exhibits only minimum data transfers related to necessary communications.

(ex. OSD interaction: monitoring the status of each OSD)

Conclusion

- We studied the overheads imposed by erasure coding on a

distributed SSD array system.

- In contrast to the common expectation on erasure codes, we

observed that they exhibit heavy network traffic and more I/O

amplification than replication.

- Also erasure coding requires much more CPU cycles and

context switches than replication due to user-level

implementation.


0s 50s 100s 150s0

600

1200

0s 20s 40s 60s 80s

0.0M

0.8M

1.6M0

30

60Overwrites

Th

rou

gh

pu

t

(MB

/s)

RX

TX

Writes on pristine

RX

TX

Co

nte

xt

Sw

itch

(# / s

ec)

#Context Switch/sec

#Context Switch/sec

CP

U

Utiliz

atio

n (

%)

User

System

User

System

Obejct initialize Obejct update

Time series analysis for CPU utilization, context switches, private network throughputobserved by random writes on pristine image & random overwrites

computer architecture and memory systems lab

Documents