jetstream: cluster-scale parallelization of …...mongodb 5.2 9 117 openoffice 7.0 10 32 firefox...

JetStream:Cluster-scaleParallelizationofInformationFlowQueries

AndrewQuinn,DavidDevecsery,PeterChenandJasonFlinn

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• DIFT instruments execution to track causality• Also known as “Taint-Tracking”

Dynamic Information Flow Tracking

Sources (Inputs)

Sinks (Outputs)

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o1

DIFT for Debugging

o1Server

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DIFT for Debugging

o1

Outputs

Inputs

Server

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DIFT for Debugging

o1

Outputs

Inputs

Server

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DIFT for Debugging

o1

Outputs

Inputs

Server

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DIFT for Debugging

o1

Outputs

Inputs

Server

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DIFT – limitations

Arnold ‘14 Overheads ~100x

X-Ray ‘12 Long queries

TaintDroid ‘10 No native code

Backtracker ‘03 Coarse-grained causality

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Parallelize DIFT

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Parallelizing DIFT is HARD

A = read()B = read()C = A + BD = X + Y E = CB = 0Z = A[D]F = E write(F)

SequentialDependencies

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Parallelizing DIFT is HARD

A = read()B = read()C = A + BD = X + Y E = CB = 0Z = A[D]F = E write(F)

SequentialDependencies

Speck (ASPLOS ‘08)

Parallel Lifeguards (ASPLOS ‘08)

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Parallelizing DIFT is HARD

A = read()B = read()C = A + BD = X + Y E = CB = 0Z = A[D]F = E write(F)

SequentialDependencies

Speck (ASPLOS ‘08)

Parallel Lifeguards (APSLOS ‘08)

“EmbarrassinglySequential”- Ruwase etal.

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JetStream

Aggregation – pipeline parallelism

Local DIFT – epoch parallelism 2xà21x

Fasterthanoriginalexecution!

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• Design of JetStream• Local DIFT• Aggregation

• Evaluation

Outline

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Debugging Query

Outputs

Inputs

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Local DIFT

Outputs

Inputs

Time slice execution into Epochs

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Local DIFT

Outputs

Inputs

Leverage Record and Replay to calculate DIFT in parallel

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Local DIFT

Outputs

Inputs

Track mapping between all intermediate locations

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Local DIFT

Outputs

Inputs

Mapping is too expensive to calculate:• log operations• defer calculating relationships until aggregation

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DIFT

D = B + C C = A[D]

• Fast Forward: replay execution until start of epoch• Analysis: log operations using a graph

CAFast Forward

Analysis

B D

CA B D

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DIFT

D = B + C C = A[D]

• Fast Forward: replay execution until start of epoch• Analysis: log operations using a graph

CAFast Forward

Analysis

B D

CA B D

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DIFT

D = B + C C = A[D]

• Fast Forward: replay execution until start of epoch• Analysis: log operations using a graph

CAFast Forward

Analysis

B D

CA B D

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Local DIFT output

Outputs

Inputs

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JetStreamLocal DIFT – epoch parallelism

Aggregation – pipeline parallelism

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Aggregation

Outputs

Inputs

Calculate paths between source and sinks• Many nodes are not on path between source and sink• Use sequential information to prune work

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Forward Pass

Outputs

Inputs

Pass locations which are derived from a source

derivedlocations

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Forward Pass

Outputs

Inputs

Pass locations which are derived from a source

derivedlocations

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Backward Pass

Outputs

Inputs

usedlocations

Pass locations which are used by a sink

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Backward Pass

Outputs

Inputs

usedlocations

Pass locations which are used by a sink

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In the paper:• Insights about why naïve approaches fail• Partitioning – challenging to predict the local DIFT time • Pre-pruning – garbage collection of the graph

JetStream

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• Evaluation

Outline

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• CloudLab cluster of 32 machines, 1–128 cores

Experimental Setup

Benchmark Sequential DIFT Time (Minutes)

Sources(millions)

Sinks(millions)

Ghostscript 1.3 3 0.2Gzip 1.8 64 488Evince 3.9 10 104Nginx 3.3 10 35Mongodb 5.2 9 117OpenOffice 7.0 10 32Firefox 30.6 0.9 2

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Benchmark Sequential DIFT Time (Minutes)

Sources(millions)

Sinks(millions)

Ghostscript 1.3 3 0.2Gzip 1.8 64 488Evince 3.9 10 104Nginx 3.3 10 35Mongodb 5.2 9 117OpenOffice 7.0 10 32Firefox 30.6 0.9 2

• CloudLab cluster of 32 machines, 1–128 cores

Experimental Setup

sources:Cookiessinks:suspiciousconnections

sources:homedirectorysinks:all

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• Unexpected analysis:• prioritize low record overhead

• Expected analysis• periodic checkpoint• gather partitioning stats

Two Different Scenarios

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1

10

100

1 10 100

Normalize

dSped

up

NumberofCoresGzip Ghostscript Evince MongodbNginx OpenOffice Firefox Ideal

mean:13x

Scalability of Unexpected Analysis

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1

10

100

1 10 100

Normalize

dSpeedu

p

NumberofCoresGzip Ghostscript Evince MongodbNginx OpenOffice Firefox Ideal

mean:21x

Scalability of Expected Analysis

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JetStream

Aggregation – pipeline parallelism

Local DIFT – epoch parallelism 2xà21x

Fasterthanoriginalexecution!

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Questions

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Related Work

Epoch Parallelism• Wallace and Hazelwood, “SuperPin: Parallelizing

Dynamic Instrumentation for Real-Time Performance”

Local DIFT• Ruwase et al. “Parallelizing Dynamic Information Flow

Tracking”• Nightengale et al. “Parallelizing security checks on

commodity hardware”

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Benchmarks

Benchmark Replay Time (seconds)

JetStream Time(seconds)

Ghostscript 1.0 5.6Gzip 3.0 2.3Evince 13.5 19.5Nginx 4.8 5.6Mongodb 22.8 13.8OpenOffice 7.6 25.0Firefox 67.4 94.4

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Aggregation Results

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BackwardsPass(seconds)

BothPassesTime(seconds)

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FastForward Instrumentation Analysis Pre-pruneForwardPass Prune BackwardPass

Gzip

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0.5

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1.5

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2.5

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3.5

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0 20 40 60 80 100 120

GzipFirstQuery

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0.5

1

1.5

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2.5

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3.5

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0 20 40 60 80 100 120

GzipSecondQuery

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FastForward Instrumentation Analysis Pre-pruneForwardPass Prune BackwardPass

OpenOffice

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10

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25

30

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0 20 40 60 80 100 120

OpenOfficeFirstQuery

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0 20 40 60 80 100 120

OpenOfficeSecondQuery