The Convergence of Mobile and Cloud Computing Ramesh Govindan [email protected] University of Southern California 1.

The Convergence of Mobile and Cloud Computing

Ramesh [email protected]

University of Southern California

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A Seductive Question…

Network Fabric

The CloudWhen 4 billion smartphones are Internet-connected, how (if at all)

should the Internet architecture change?

Bridging the Capability Gap

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Capability

Time

As the Smartphone becomes the primary computing device…As the Smartphone becomes the primary computing device…

What the device is capable ofWhat the device is capable of

What users will wantWhat users will want

Bridging the Capability Gap

Dealing with ConstraintsDealing with Constraints

Energy-Delay Tradeoffs in a Video Documentation SystemEnergy-Delay Tradeoffs in a Video Documentation System

Overcoming ConstraintsOvercoming Constraints

Toward Cloud-Assisted Interactive Mobile PerceptionToward Cloud-Assisted Interactive Mobile Perception

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Energy Constraint

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Battery Life is an issue for UsabilityBattery Life is an issue for Usability

Opportunity: Multiple peripherals and sensorsOpportunity: Multiple peripherals and sensors

Intelligently use peripherals and sensors to reduce energy

consumption

Video Collection 10 years ago

Video Collection Today

M. Ra, J. Paek, A. Sharma, R. Govindan, M. Krieger, M. Neely, “Energy-Delay Tradeoffs in Smartphone Applications”, ACM Mobisys 2010.

Transportation SecurityPost-Disaster Urban Planning

Documenting Post-Katrina

Reconstruction

Delay-ToleranceDelay-Tolerance

Many of our users are delay-tolerantTransportation Security

Transportation Security

Dealing withChild development

Issues

Dealing withChild development

Issues

Planning ResearchPlanning Research

But tolerance varies considerably

DownloadDownload UploadUploadDelay-Tolerant

Transferring Large Volumes of DataTransferring Large Volumes of Data

Leverage Delay ToleranceLeverage Delay Tolerance

Reduce the energy costReduce the energy cost

EDGE/3G WiFi

Energy(J/bit)

Availability

ChannelQuality

HIGHHIGH LOWLOW

HIGHHIGH LOWLOW

Time-VaryingTime-Varying Time-VaryingTime-Varying

Delay transmission

Adapt to wireless channel quality

EDGE 3G WiFi

video 1arrives

video 2arrives

40 KB/s

200 KB/s

50 KB/s

10 KB/s

TIMETIME

TIMETIME

EDGE 3G WiFi

video 1video 2

DelayDelayEnergyEnergy

246246242242

9595

305305

5050

320320

JJ secsec

Min-DelayWiFi-OnlyEnergy-Optimal

Optimal can save significant energyOptimal can save significant energy

MDMD MEME EOEO MDMD MEME EOEO

Challenge: How to design the optimal trade-off algorithm?

Challenge: How to design the optimal trade-off algorithm?

Whether, When, WhichWhether, When, Which

Delayed Transmission

Delayed Transmission

TunableDelay-Tolerance

TunableDelay-Tolerance

SALSA

Ignore link qualityIgnore link quality

SALSAKnow-WiFiStatic-DelayWiFi-onlyMin-Delay

Ignore queue backlogIgnore queue backlog

Since SALSA takes all factors into account,

it performs closest to the optimal

Since SALSA takes all factors into account,

it performs closest to the optimal

Additional Delay

Gain? Loss?Save 2% ~ 80% of battery capacity

Save 2% ~ 80% of battery capacity

+ 2 min ~ 2 hour

+ 2 min ~ 2 hour

Energy Savings

Battery

Bridging the Capability Gap

Dealing with ConstraintsDealing with Constraints

Energy-Delay Tradeoffs in a Video Documentation SystemEnergy-Delay Tradeoffs in a Video Documentation System

Overcoming ConstraintsOvercoming Constraints

Toward Cloud-Assisted Interactive Mobile PerceptionToward Cloud-Assisted Interactive Mobile Perception

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- Interactive. (low latency 10 ~ 100ms)- High data rate. (media data)- Computationally Intensive. (ML/Vision-based algorithms)

- Interactive. (low latency 10 ~ 100ms)- High data rate. (media data)- Computationally Intensive. (ML/Vision-based algorithms)

M. Ra, A. Sheth, L. Mummert, P. Pillai, D. Wetherall, Odessa: Enabling Interactive Perception Applications on Mobile Devices, ACM MobiSys 2011

Challenge

ConstraintConstraint

Many perception applications require significant compute power Many perception applications require significant compute power

Overcoming the constraintOvercoming the constraint

Offload computation to the “cloud”Offload computation to the “cloud”

Offloading decision is non-trivial, since wirelessnetwork bandwidth is also constrained

Offloading decision is non-trivial, since wirelessnetwork bandwidth is also constrained

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Odessa

A system that automatically and dynamicallyoffloads components of a perception data-flow to the cloud

A system that automatically and dynamicallyoffloads components of a perception data-flow to the cloud27

Applications

Face RecognitionFace Recognition

Pose EstimationPose EstimationGesture RecognitionGesture Recognition

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Odessa Goals and Techniques

High Throughput (frames per second) Low Latency (time to process a single frame)

High Throughput (frames per second) Low Latency (time to process a single frame)

OffloadingParallelismOffloadingParallelism

Motivation: Offloading

Variable on Inputsfor Face Recognition

Variable on Inputsfor Face Recognition

Variability on network conditionsVariability on network conditions

Variability on different devicesVariability on different devices

Offloading decision should be adaptive.A static decision may not work.

Offloading decision should be adaptive.A static decision may not work.

Parallelism1. Data Parallelism

2. Pipeline Parallelism

Frame 1Frame 1

Frame 2Frame 2 Frame 1Frame 1Frame 3Frame 3

Frame 1Frame 1

Frame 2Frame 2

Frame 3Frame 3

ThroughputMakespanThroughputMakespan

ThroughputThroughput

Motivation: Data Parallelism

Accuracy and execution time of face detection stage (face recognition)

Avg. execution time of SIFT feature extraction stage.(object and pose recognition)

The level of data parallelism affects accuracy and performance much,

and it should consider precedence partition.

The level of data parallelism affects accuracy and performance much,

and it should consider precedence partition.

Motivation: Pipelining

- Desirable # of tokens may be different.- How do we even know right # of tokens a priori?- Desirable # of tokens may be different.- How do we even know right # of tokens a priori?

A static choice of pipeline parallelism can lead to suboptimal makespan

or leave the pipeline underutilized.

A static choice of pipeline parallelism can lead to suboptimal makespan

or leave the pipeline underutilized.

Odessa Design

Lightweight Application Profiler

Lightweight Application Profiler

Uses statistics from application pipeline to make decisions

Uses statistics from application pipeline to make decisions

Decision EngineDecision EngineAdapts data parallelism

and stage offloading.Adapts data parallelism

and stage offloading.

Adaptspipeline parallelism

Adaptspipeline parallelism

Profiler

Stage

Stage

Stage Stage

Stage

Stage

Start Stage

DecisionEngine

DecisionEngine

PiggybackingPiggybacking

Suppressduplicatedinformation

Suppressduplicatedinformation

Report statistics to the decision

engine per frame

Report statistics to the decision

engine per frame

Report

Light-weightLight-weight

Cycles,Parallel Stages

Cycles,Parallel Stages

Decision Engine

Pick a Bottleneck Stage (Start Point)

Compute Stage?

Estimate Necessary Costs

Choose the Best Choice; Offload, Spawn or Do nothing.

Bottleneck is Network Edge

Considerdata parallelism and

stage offloadingsimultaneously

Considerdata parallelism and

stage offloadingsimultaneously

No

Yes

Incremental Algorithm

Incremental Algorithm

Evaluation

ThroughputThroughput

MakespanMakespan

Odessa finds an optimal configuration automatically.

Odessa finds an optimal configuration automatically.

Comparison

FPS: Higher is betterFPS: Higher is better Makespan: Lower is betterMakespan: Lower is better

Odessa shows 3x improvement over competing strategies

Odessa shows 3x improvement over competing strategies

Convergence

Interesting research opportunities at theboundary between mobile and cloud computing

Interesting research opportunities at theboundary between mobile and cloud computing

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