Comparing Coordinated Garbage Collection

Algorithms for Arrays of Solid-state Drives

Comparing Coordinated Garbage Collection

Algorithms for Arrays of Solid-state Drives

Junghee Lee, Youngjae Kim, Sarp Oral, Galen M. Shipman, David A. Dillow, and Feiyi Wang

Presented by Junghee Lee

2

High Performance Storage SystemsHigh Performance Storage Systems

• Server centric services– File, web & media servers, transaction processing servers

• Enterprise-scale Storage Systems– Information technology focusing on storage, protection, retrieval of

data in large-scale environments

Google's massive server farms High PerformanceStorage Systems

Storage UnitHard Disk Drive

3

Emergence of NAND Flash based SSDEmergence of NAND Flash based SSD

• NAND Flash vs. Hard Disk Drives– Pros:

• Semi-conductor technology, no mechanical parts

• Offer lower access latencies– μs for SSDs vs. ms for HDDs

• Lower power consumption• Higher robustness to vibrations and

temperature– Cons:

• Limited lifetime– 10K - 1M erases per block

• High cost– About 8X more expensive than current

hard disks• Performance variability

MacBook Air

Failure

Read

Smal

l

Writ

e

4

Intel X25-E 64GB SSD

Fusion io 640GBMLC PCIe DUO ioDrive

SSD-based Object Storage Target (OST)SSD-based Object Storage Target (OST)

• SSD-based OSTs– PCI Express SSDs

• Fusion IO ioDrive, Virident tachIOn, OCZ RevoDrive, etc

– SATA SSDs• Intel, SuperTalent, Samsung, etc

• PCIe SSDs versus RAID of SATA SSDs

PCIE SSD (Fusion IO)

Array of SATA SSDs

Performance Cost

High

HighRelatively

Cheap

SSD Type

Expensive

$13,990/640GB

$799/64GB

~1.3GB/s

~280MB/s

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Efficiency Analysis of SSD RAIDEfficiency Analysis of SSD RAID

• RAID of SSDs– Configured 6 SSDs in RAID-0 using Mega RAID controller– Mega RAID controller is only able to connect up to 6 SSDs.

• Cost efficiency analysis– Metric (GB per $ and MB/s per $)– Compared RAID-0 of 6 x SATA SSDs versus 1 x PCIE SSD

• SSDs used

MLC SSD

SLC SSD

SpecificationSize (GB)

Super-Talent MLC SATA II SSD

Intel SLC SATA II SSD 64

SSD Type

120

PCIe SSD Fusion-io ioDrive Duo MLC PCIe x8 SSD 640

Price ($)

799

415

13,990

6

Capacity Efficiency AnalysisCapacity Efficiency Analysis

• Total cost – N (RAID controller) x $ (RAID controller) + N (SSD) x $ (SSD)– We used $579 for PCIE LSI Mega RAID controller card.

Series10

1

2

3

4

5

6

GB per dollar

6 MLC SSDs (RAID-0) 6 SLC SSDs (RAID-0) 1 PCIe SSD

Device Type

No

rma

lize

d E

ffie

nc

y

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Performance Efficiency AnalysisPerformance Efficiency Analysis

• Total cost – N (RAID controller) x $ (RAID controller) + N (SSD) x $ (SSD)– We used $579 for PCIE LSI Mega RAID controller card.

0 20 40 60 80 1000

1

2

3

4

5

6

MB/s per dollar

6 MLC SSDs (RAID-0) 6 SLC SSDs (RAID-0) 1 PCIe SSD

Read (%)

No

rma

lize

d E

ffie

nc

y

8

Lessons LearnedLessons Learned

• From the cost-efficiency analysis, we learned:– RAID of SSDs is more cost-efficient than PCIE SSD in terms of capacity

per dollar and bandwidth per dollar. – In particular, MLC based SSDs in RAID is more cost-efficient than SLC

based SSDs.

• Then what are problems and challenges in SSD RAID?– Does SSD RAID offer sustainable bandwidth?– If not, why not? Any solution?

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RAID of SSDsRAID of SSDs

• Problems – Overall bandwidth of RAID of SSDs is dependent

on the slowest SSD.– During garbage collector (GC) is working,

incoming requests cannot be serviced.– GC process of each SSD in RAID of SSDs is

is not globally coordinated.• Challenges

– There is no functional support for coordinating individual GC processes at the conventional RAID controller.

– We need to develop a mechanism for RAID controller to be able to coordinate individual GC processes in RAID of SSDs.

• Idea and Solution– Harmonia! – A Coordinated Garbage Collector for RAID of SSDs

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Uncoordinated Garbage CollectorsUncoordinated Garbage Collectors

Time

Local GC process Aggregate degraded performance

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Bandwidth Drop for Write-Dominant WorkloadsBandwidth Drop for Write-Dominant Workloads• Experiments

– Measured bandwidth for 1.87 MB by varying read-write ratio (qd=64)

RAID-0 of MLC SSDs RAID-0 of SLC SSDs

Performance variability increases as we increase write-percentage of workloads.

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Performance VariabilityPerformance Variability

• Per-Drive Bandwidth (MB/s per drive)

1 SSD 4 SSDs 6 SSDs0

50

100

150

200

250

300

RAID of SSDs (M) Linear (RAID of SSDs (M))RAID of SSDs (S) Linear (RAID of SSDs (S))

Pe

r-D

riv

e B

an

dw

idth

(M

B/s

)

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A Globally Coordinated Garbage CollectorA Globally Coordinated Garbage Collector

Time

Local GC process Aggregate degraded performance

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Inclusive CoordinationInclusive Coordination

Threshold

Free blocks Free blocks Free blocks Free blocks

Request

Force GC

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Selective CoordinationSelective Coordination

Soft

Free blocks Free blocks Free blocks Free blocks

RegisterForce GC

Hard

Request

Register

16

Proactive MethodProactive Method

Soft

Free blocks Free blocks Free blocks Free blocks

Force GC

Hard

Idle Detected

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Design of HarmoniaDesign of Harmonia

• SSD optimized RAID controller (O-RAID)– A RAID controller designed to enable global coordination of

garbage collection when used with SSDs supporting that capability.

• Global GC optimized SSD (O-SSD)– An SSD designed for participating in a globally coordinated

garbage collection process in an O-RAID. • Extension of storage protocols

– Extension of storage protocols such as SATA and SCSI for controlling the additional capabilities of O-SSD device

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Experimental SetupExperimental Setup

• Simulator– MSR’s SSD simulator based on DiskSim– Configured RAID-0 of 8 32GB SSDs using 4KB Stripe unit size

• Workloads– HPC-like Synthetic workloads

• Used the synthetic workload generator in DiskSim• HPC (W): 80% Writes, HPC (R): 80% Reads

– Enterprise-scale Realistic workloads

WorkloadsAverage request

size (KB)Read ratio

(%)

Arrival rate

(IOP/s)Financial 7.09 18.92 47.19

Cello 7.06 19.63 74.24

TPC-H 31.62 91.80 172.73

OpenMail 9.49 63.30 846.62

Write dominant

Read dominant

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Results for HPC-like Synthetic WorkloadsResults for HPC-like Synthetic Workloads

HPC (W) HPC (R)0

0.2

0.4

0.6

0.8

1

1.2

BaselineGGC

No

rma

lize

d A

ve

rag

e

Re

sp

on

se

Tim

e

HPC (W) HPC (R)0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

BaselineGGC

Sta

nd

ard

De

via

tio

nResponse time improvements are 69% and 55% for HPC(W) and HPC(R) workloads respectively.

Significant improvement on standard deviations by GGC

69%55%

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Results for Realistic WorkloadsResults for Realistic Workloads

Openm

ail

TPC-H

Finan

cial

Cello

0

0.2

0.4

0.6

0.8

1

1.2

BaselineGGC

No

rma

lize

d A

ve

rag

e R

es

po

ns

e

Tim

e

Openm

ail

TPC-H

Finan

cial

Cello

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

BaselineGGC

Sta

nd

ard

De

via

tio

n

Performance improvement is about 10%.

Standard deviation significantly improves by GGC.

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Comparison of Coordination AlgorithmsComparison of Coordination Algorithms

Evenly dis-trib-uted

Skewed Bursty0

0.2

0.4

0.6

0.8

1

1.2

Number of Erase

InclusiveSelectiveProactive

Evenly dis-trib-uted

Skewed Bursty0

0.2

0.4

0.6

0.8

1

1.2

Average Response Time

InclusiveSelectiveProactive

Workload characteristics Workload characteristics

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ConclusionsConclusions

• Empirical experiments using real SSDs– We showed that RAIDs of SSDs exhibit high performance variability due to

uncoordinated GC processes. • Harmonia: A coordinated garbage collector

– We proposed Harmonia, a global garbage collector, that coordinates the local GC process of the individual SSDs.

• Comparison of coordination algorithms– Selective method reduces the number of erase compared to the inclusive

method when the workload is skewed– Proactive method enhances the response time when the workload is bursty

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Thank you!Thank you!

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Questions?Questions?

Contact info

Junghee Leejunghee.lee@gatech.eduElectrical and Computer EngineeringGeorgia Institute of Technology