Intentional Networking: Opportunistic Exploitation of - PowerPoint Presentation

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Intentional Networking: Opportunistic Exploitation of Mobile Network Diversity

T.J. GiuliDavid Watson

Brett Higgins

Azarias Reda Timur AlperovichJason Flinn Brian NobleSlide2

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Diversity of networks

Diversity of behavior

Email

Fetch messages

The Challenge

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YouTube

Upload video

Match traffic to available networksSlide3

Current approaches: two extremes

All details hidden All details exposed Result: mismatched Result: hard for traffic applications

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Please

insert

packets

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SolutionSystem measures available networks

Applications describe their trafficSystem matches traffic to networksBrett Higgins4Slide5

Separate concernsSystem monitors and measures available networksApps describe their traffic

System matches traffic to the right networkBe simpleApps use qualitative labels like FG/BG, small/largeEmbrace concurrencyConsider multiple networks like multiple coresAbstractions for network concurrency5

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Intentional NetworkingSlide6

RoadmapMotivation

AbstractionsApplicationsEvaluationSummary6Brett HigginsSlide7

Abstraction: Multi-socketSocket: logical connection between endpoints

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Client

Server

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Abstraction: Multi-socketMulti-socket:

virtual connectionMeasures performance of each alternativeEncapsulates transient network failure8

Client

Server

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Abstraction: LabelQualitative description of network traffic

Size: small (latency) or large (bandwidth)Interactivity: foreground vs. background9Brett Higgins

Client

ServerSlide10

Implementing “foreground”

Foreground traffic takes precedence

Need to keep BG traffic out of the wayBorrow from anticipatory scheduling

10Kernel socket bufferData buffered in library

BG

FG

≤ 50ms of BG data

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If no FG data,

increase to 1 secSlide11

Abstraction: IROBIROB: Isolated

Reliable Ordered BytestreamGuarantees atomic delivery of data chunkApplication specifies data, atomicity boundary11

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432

1

IROB 1Slide12

Abstraction: Ordering ConstraintsApp specifies partial ordering on

IROBsReceiving end enforces delivery order12Brett Higgins

Server

IROB 1

IROB 3

IROB 2

IROB 2

IROB 3

Dep

: 2

IROB 1

use_data

()Slide13

Abstraction: ThunkWhat happens when traffic should be deferred?

Application passes an optional callback + stateBorrows from PL domainIf no suitable network is available:Operation will fail with a special codeCallback will be fired when a suitable network appearsUse case: periodic background messagesSend once, at the right time

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Intentional Networking: AbstractionsMulti-socket:

virtual connectionLabel: qualitative message descriptionIROB: atomicity and ordering constraintsThunk: expose network events to application

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RoadmapMotivation

AbstractionsApplicationsEvaluationSummary15Brett HigginsSlide16

ApplicationsDistributed file system

File reads (foreground) and writes (background)Reads and writes unordered with respect to each otherIMAP email + prefetchingUser requests (foreground) and prefetches (background)Add small/large label for responsesVehicular sensingRoad condition monitoring (small, foreground)Vehicle performance trend analysis (large, background)

Use thunks to decide when to send cumulative sensor data

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Applications

Distributed file systemFile reads (foreground) and writes (background)Reads and writes unordered with respect to each otherIMAP email + prefetchingUser requests (foreground) and prefetches (background)Add small/large label for responsesVehicular sensing

Road condition monitoring (small, foreground)Vehicle performance trend analysis (large, background)Use thunks

to decide when to send cumulative sensor data17Brett HigginsSlide18

RoadmapMotivation

AbstractionsApplicationsEvaluationSummary18Brett HigginsSlide19

Evaluation: MethodologyGathered network traces in a moving vehicle

Sprint 3G & open WiFiBW up/down, RTTReplayed in lab (trace map here)19

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Evaluation: Comparison StrategiesGenerated from the network traces

Idealized migrationAlways use best-bandwidth networkAlways use best-latency networkIdealized aggregationAggregate bandwidth, minimum latencyUpper bounds on app-oblivious performance20

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Evaluation Results: Email

Trace #2: Ypsilanti, MI

3%

7x

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Evaluation Results: Vehicular SensingTrace #2: Ypsilanti, MI

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48%

6%Slide23

Evaluation Results: Vehicular SensingTrace #2: Ypsilanti, MI

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SummaryIntentional Networking

Separates concerns between apps and the systemApplications describe their network trafficSystem matches traffic to the right networkSimplifies app task with qualitative labelsProvides abstractions for network concurrencyOutperforms all application-oblivious strategiesQuestions?

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Evaluation Results: Vehicular SensingTrace #2: Ypsilanti, MI

26Single-process

Multi-processUrgent Update Latency

Background ThroughputSingle-processMulti-process

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Evaluation Results: EmailBenchmark

User opens 5 emails (foreground)App prefetches 100 emails (background)We report:Average on-demand fetch timeTime to finish prefetching all 100 emails27

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Evaluation Results: EmailTrace #1: Ann Arbor, MI

1%

11x

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Evaluation Results: Vehicular SensingTrace #1: Ann Arbor, MI

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1%

4x

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Evaluation Results: Vehicular Sensing

Trace #2: Ypsilanti, MI30

6%

2x

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Evaluation Results: Multiple ProcessesTrace #1: Ann Arbor, MI

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Single-process

Multi-processUrgent Update LatencyBackground ThroughputSingle-processMulti-process

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Vehicular SensingBased on Ford research platform specs

Raw sensor data sent to central serverAbout 25KB/sec of dataUseful for long-term analysis, preventative maintenanceSome sensor readings indicate abnormal conditionsTraction control engaging => slippery roadsSend these reports to service to alert other drivers32

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