Download pdf - Mobile Cloud Offloading - Kangwoncs.kangwon.ac.kr/~ysmoon/courses/2017_1/grad/09.pdfMobile Edge Computing Mobile Cloud Offloading, [email protected] 5/1/17 41 Mobile Edge Computing

상지대학교 컴퓨터공학과고 광 만

Mobile Cloud Offloading

MobileCloudOffloading,[email protected] 5/1/17

People Truly Love Their Smartphones

Mobile Cloud Offloading, [email protected]

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5/1/17

Source of Dissatisfaction

Mobile Cloud Offloading, [email protected]

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5/1/17

Process Performance Improvement

5/1/17Mobile Cloud Offloading, [email protected]

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Battery Lifetime Capacity Improvement


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Reasons for SLOW Enhancement in Battery Lifetime


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Batterycapacityiscontinuallyincreasing

EnergyEfficiencyISSUES

But,powerconsumptionofamobiledevicesremainssteadyduetodemandfornewhardwaretechnologies¤ Higherresolutiondisplay¤ HigherpixelCamera¤ Fasterwirelessnetwork¤ NewsensorsincludingNFC,fingerprintscanner,etc.


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ForthcomingApplications,SW,technologiesdemandextremelyhigherperformance

¤ MultimediaProcessing¤ Holographicand3Dmediaprocessing¤ Bigdataprocessing¤ AugmentedReality

Mobile apps can’t reach their full potential


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AugmentedReality

SpeechRecognitionandSynthesis InteractiveGames

Slow,LimitedorInaccurate

TooCPUintensive Limited

PowerIntensive

Notonparwithdesktopcounterparts

Problems Ahead


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WEwant

¤ tokeeppowerconsumptionconstraint

¤ todrasticallyboostupperformance

Computation Offloading


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Let’sshiftdifficultanddirtyworkstosomeoneelse,CLUOD.

Computation‐intensiveOffloading

Computation Offloading Architecture


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Offloadingsolutiontoaugmentthesemobilesystems’capabilitiesbymigratingcomputationtomoreresourcefulcomputers(i.e.,servers)thisisdifferentfromthetraditionalclient‐serverarchitecture.

Asignificantamountofresearchhasbeenperformedoncomputationoffloading:makingitfeasiblemakingoffloadingdecisionsdevelopingoffloadinginfra‐ structures

■ Issue1,■ Offloadingmaysaveenergyandimproveperformanceonmobilesystems.

■ However,thisusuallydependsonmanyparameters■ thenetworkbandwidths■ theamountsofdataexchangedthroughthenetworks.

■ Manyalgorithmshavebeenproposedtomakeoffloadingdecisionstoimproveperformanceorsaveenergy

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■ Issues2,■ Offloadingrequiresaccesstoresourcefulcomputersforshortdurationsthroughnetworks,wiredorwireless.

■ Theseserversmayusevirtualizationtoprovideoffloadingservicessothatdifferentprogramsandtheirdatacanbeisolatedandprotected.

■ Issues3,■ Offloadingmaybeperformedatthelevelsof

■ methods,■ tasks■ applications■ virtualmachines

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■ Thecommonapproachesusedtomakeoffloadingdecisions:■ whytooffload(improveperformanceorsaveenergy)■ whentodecideoffloading(staticvsdynamic)■ whatmobilesystemsuseoffloading(laptops,PDAs,robots,sensors)

■ typesofapplications(multimedia,gaming,calculators,texteditors,predictors)

■ infrastructuresforoffloading(gridandcloudcomputing).

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Case Study: Face‐detection


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Case Study: Real‐time Subtitle Generator


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More Offloading Extension


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● MobileCloudComputing(MCC)● ComputationOffloading+CapabilityExtension

● Computation‐intensiveOffloading● Improveperformanceandenergyefficiency● Communication‐intensiveOffloading

● CapabilityExtension● Enableimpossiblethingsbyusingcloudresourceotherthan

processor

Comparison among RPC, RMI, .Net Remoting and Offloading


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Types of Computation Offloading


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Method‐level Offloading


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MAUI: Mobile Assistance Using Infrastructure


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MAUIContributions:CombineextensiveprofilingwithanILPsolver¤ Makesdynamicoffloaddecisions¤ Optimizeforenergyreduction¤ Profile:device,network,application

Leveragemodernlanguageruntime(.NETCLR)¤ Tosimplifyprogrampartitioning¤ Reflection,serialization,strongtyping

MAUI Architecture


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Maui serverSmartphone

Application

ClientProxy

Profiler

Solver

MauiRuntime

ServerProxy

Profiler

Solver

Maui Runtime

Application

RPC

RPC

MauiController

How Does a Programmer Use MAUI?


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Goal:makeitdead‐simpletoMAUI‐ifyapps¤ Buildappasastandalonephoneapp¤ Add.NETattributestoindicate“remoteable”¤ Followasimplesetofrules

Language Run‐Time Support For Partitioning


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Portability:¤ Mobile(ARM)vsServer(x86)¤ .NETFrameworkCommonIntermediateLanguage

Type‐SafetyandSerialization:¤ Automatestateextraction

Reflection:¤ Identifiesmethodswith[Remoteable]tag¤ AutomatesgenerationofRPCstubs

MAUI Profiler


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ProfilerCallgraph

ExecutionTime

Statesize

yNetworkLatency

hNetworkBandwidth

DeviceProfileCPUCycles

NetworkPowerCostNetworkDelay

ComputationalDelay

ComputationalPowerCostComputationalDelay

AnnotatedCallgraph

Is Global Program Analysis Needed?


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Yes!– ThissimpleexamplefromFaceRecognitionappshowswhylocalanalysisfails.

FindMatch900mJ

InitializeFaceRecognizer5000mJ

1000mJ

DetectAndExtractFaces

15000mJ

UserInterface

Cheapertodolocal


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FindMatch

InitializeFaceRecognizer

1000mJ

DetectAndExtractFaces

UserInterface

25900mJ

Cheapertooffload

Can MAUI Adapt to Changing Conditions?


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Adaptto:¤ NetworkBandwidth/LatencyChanges¤ Variabilityonmethod’scomputationalrequirements

Experiment:¤ Modifiedofftheshelfarcadegameapplication¤ PhysicsModeling(homingmissiles)¤ Evaluatedunderdifferentlatencysettings

How much can MAUI improve performance?


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FaceRecognizer

Latency to server impacts the opportunities for fine‐grained offload


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Upto40%energysavingsonWi‐Fi

SolverwoulddecidenottooffloadArcadeGame

Can MAUI Run Resource‐Intensive Applications?


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CPUIntensiveevenonaCore2DuoPC

CanberunonthephonewithMAUI

Translator

Can MAUI Run Resource‐Intensive Applications?


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CPUIntensiveevenonaCore2DuoPC

CanberunonthephonewithMAUI

Translator

Cuckoo: A Computation Offloading Framework


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COMET: Code Offload by Migration Execution Transparently


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Is Offloading Really Effective ??


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Mobile Edge Computing


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● MobileEdgeComputingenablesmobilesubscriberstoaccessITandcloudcomputingservicesatthecloseproximitywithintherangeofRadioAccessNetwork(RAN).

● ThemaingoalofMobileEdgeComputingtoreducelatencybybringingthecomputationandstoragecapacityfromthecoreWANtotheedgenetwork.

● MobileEdgeComputingcanbedefinedas

“MobileEdgeComputingisamodelforenablingbusinessoriented,cloudcomputingplatformwithintheradioaccessnetworkatthecloseproximityofmobilesubscriberstoservedelaysensitive,contextawareapplications.”


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Enpiler: Energy‐aware offloading framework through comPiler code analysis techniques


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DisCo: Distributed and Concurrent Offloading Framework for Smart IoT Applications in Mobile Edge Cloud Computing Environments


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Mobile EdgeServer - 2


Mobile EdgeServer - n


(AR/VR Gaming Processing)

Mobile Device

Mobile Edge Server Side

(Sensing BigdataProcessing)

Mobile Device

(Multimedia Image Processing)

Mobile Device

Concurrent Modules

Distributed Offloading

Concurrent Modules

Concurrent Modules

Key ManagementServer

Selected Publications


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■ 고광만(교신) 외 4인, "Enabling Far-Edge Analytics: Performance Profiling of Frequent Pattern Mining Algorithms," Sensors(IF: 2.033, Q2), April 2017.

■ 고광만(교신) 외 4인, "SIMDOM: A Framework for SIMD Instruction Translation and Offloading in Heterogeneous MCC Architectures," Transactions on Emerging Telecommunications Technologies (IF: 1.130, Q1), February 2017.

■ 고광만(교신) 외 3인, Optimization Techniques to Enable Execution Offloading for 3D Video Games", Multimedia Tools and Applications(IF: 1.345, Q2), July 2016.

■ 고광만(공동) 5인, "A Case and Framework for Code Analysis Based Mobile Application Energy Estimation," International Journal of Communication Systems (IF 1.02, Q3), December 2016.

■ 고광만(공동) 외 6인, "Towards native code offloading based MCC frameworks for multimedia applications: A survey," Journal of Network and Computer Applications(IF: 2.413, Q1), November 2016.

■ 고광만(교신), "Reduction of Media Servers Overload with Energy-saving Adaptive Streaming", Mobile Information Systems(IF: 1.463, Q2), Article ID 7462821, Jan., 2016.


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■ Q&A■ ThankYou