Augmenting Complex Problem Solving

with Hybrid Compute Units

Hong-Linh Truong, Hoa Khanh Dam, Aditya Ghose, SchahramDustdar

Distributed Systems Group, Vienna University of Technology

University of Wollongong

truong@dsg.tuwien.ac.athttp://dsg.tuwien.ac.at/research/viecom

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Outline

Motivation

Programming hybrid, collective adaptive systems in

the cloud

Hybrid compute units

Fundamental programming elements and constructs

Framework

Illustrating examples

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Hybridity and

Diversity of

Computing ModelsMachine-based

Computing

Human-based

Computing

Things-based

computing

Grid

Pro

cessin

g

Unit

Pro

cessin

g

Unit

Arc

hitectu

reA

rchitectu

reC

om

m.

Com

m.

SMP

S. Dustdar, H. Truong, “Virtualizing Software and Humans

for Elastic Processes in Multiple Clouds – a Service

Management Perspective”, in International Journal of Next

Generation Computing, 2012

Ad hoc networks Web of things

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Today’s complex problems need

hybridity and diversity-aware “collective

adaptive systems” (HDA-CAS)

Today’s complex problems need

hybridity and diversity-aware “collective

adaptive systems” (HDA-CAS)

Motivation -- Dynamic hybridity in a

collective Mixture of different types of resources working in

concert in the same collective

Mixture of different roles performed in the same

collective

Different functions based on different capabilities

Mixture of different quality from a single collective

Functions are performed under different quality

Resources possible offer different quality wrt their

role

Mixture of cost/benefit models

A collective might perform a function with different

costs/qualities

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Motivation – HAD-CAS

Challenges/difficulties in programming HDA-

CAS in the cloud Human-in-the-loop

Elasticity of costs, quality/benefits and resources

Unified framework for humans and software

Cross-platform with complex underlying APIs

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Open problem: there is a lack of suitable programming

frameworks

Open problem: there is a lack of suitable programming

frameworks

Our goals

Construct hybrid collective adaptive systems

Consist of service units abstracting software, people

and things

With different service units roles (computation, data,

management)

Be elastic in multi-cloud environments

Provide common programming features/APIs that

enable different coordination/composition ways for

CASs

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Our approach: hybrid compute units for collective adaptive

systems (hCAS)

Our approach: hybrid compute units for collective adaptive

systems (hCAS)

Unified service unit model for

capturing hybridity in a collective

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Modeling type of

units (e.g.,

computation, data,

monitor,) and their

dependencies

Consumption,

ownership, provisioning,

price, etc.

ElasticService

Unit

Service model

Unit Dependency

ElasticCapability

Function

The functional

capability of the unit

and interface to

access the function

Capabilities to be elastic

under different

requirements

SoftwareSoftware PeoplePeople

VolunteersProfessionals

ThingThing

Resources

Hybrid compute units for CAS

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Hybrid compute unit (HCU): a set of service units

includes software-based services, human-based

services and things-based services that can be

provisioned, deployed and utilized as a collective

on-demand based on different quality, pricing and

incentive models.

Hybrid compute unit (HCU): a set of service units

includes software-based services, human-based

services and things-based services that can be

provisioned, deployed and utilized as a collective

on-demand based on different quality, pricing and

incentive models.

This paper focuses on abstracting service units and

relationships to provide fundamental programming elements

for HCU

Hybrid compute unit design –

fundamental elements

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Hybrid compute unit design --

Relationships

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Relationship Type HBS SBS TBS HCU

Similarity Yes Yes Yes Yes

Composition Yes Yes Yes Yes

Data Dependency Yes Yes Yes Yes

Control Dependency Yes Yes Yes Yes

Location Dependency Yes Yes Yes Yes

Forwarding Yes Yes No Yes

Delegation Yes Yes No Yes

Social Relation Yes No No Yes

Elasticity Yes Yes No Yes

Hybrid compute unit design – high-

level programming constructs

Current prototype: Fundamental elements can

be implemented as classes in high-level

programming languages

Service units and their properties

Relationship constructs

But complex runtime is still open

Relationships require complex code libraries for

different templates

Complex integration with existing clouds

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Hybrid compute unit design – high-

level programming constructs

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Relationships Constructs

Similarity similarity(U; V; criteria)

?similarity(U; x; criteria)

Data Dependency datadependency(U;D; [M; ]V )

?datadependency(x;D;M; V ),

?datadependency(U;D;M; x)

?datadependency(U;D; x[c]; V )

Location locationdependency(U; V; ctx; path)

Brokering delegate(U; task; V )

forward(U; task; V )

?delegate(U; task; x)

SocialRelation socialrelation(U; V; ctx; path)

?socialrelation(U; x; distance; ctx;

path).

Elasticity ?elasticity(U; elasticityReq; x)

Towards programming framework

for hCAS

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HDA-CAS Coordination

and Composition Models

HDA-CAS Coordination

and Composition Models

[ICSOC12]

Illustrating Example

Evaluating quality of data (QoD) in

simulations/data analytics processes

Typical components in simulation workflows/data

analytics

Goal using hCAS to support quality of data

control of the simulations/data analytics

Reasons: reducing wasting time and computational

cost, dealing with tacit quality evaluation, etc.

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pre-processing

solvingpost-

processingdata

analysis

hCAS and simulations

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Only SBS is needed,

for example, in the QoD evaluation step

before pre-processing

SBS or HBS is used interchangeably or in

combination

for example, in the QoD evaluation after pre-

processing,

Only HBS is used

e.g., in QoD evaluation after solving.

hCAS and simulation

workflows/data analytics

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hCAS

Excerpt -- Programming elasticity

and collectiveness in solving stepsSBS preprocessingUnit =new SBS() ; //create an instance of software unit

File data =new File(fileName);

//...

//create another unit for QoD evaluation

SBS qodEvalUnit = new SBS();

ArrayList paraType = new ArrayList();

paraType.add(File.class.getName());

//the unit must support qodEvaluate function which returns a value in [0,1]

qodEvalUnit.setFunction("qodEvaluate",paraType, Double.class.getName());

//...

//call qod evaluation unit

Double result =(Double)qodEvalUnit.execute("qodEvaluate",params);

…

//call preprocessing activity if QoD is satisfied

if (result > 0.9) {

preProcessedData=preprocessingUnit.execute("preprocessing",params);

}

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Excerpt -- Programming elasticity

and collectiveness in solving stepsif (qodPreProcessedData < 0.5) {

//initiate a new unit

ICU dataScientist = new ICU();

//create a dropbox place for sharing data

DropboxAPI<WebAuthSession> scuDropbox = null;

// ....

DropboxAPI.DropboxLink link = scuDropbox.share("/hbscloud");

//ask the cloud of HBS to invoke the ICU

VieCOMHBS vieCOMHBS = new VieCOMHBSImpl();

vieCOMHBS.startHBS(dataScientist);

HBSMessage msg = new HBSMessage();

msg.setMsg("pls. use shared dropbox for communication " +link.url);

vieCOMHBS.sendMessageToHBS(dataScientist, msg);

}

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Excerpt – Programming elasticity

and collectiveness in solving stepsif (qodPreProcessedData < 0.9) {

//specify some static properties of the solver

SBS solverUnit2 = new SBS("solver");

solverUnit2.function.put("DIRTY_DATA", Boolean.valueOf(true));

//specify expected cost and accuracy support

CostModel costModel = new CostModel();

costModel.price = 100; //max in EUR

costModel.usageTime = 1000 * 60 * 60; //1 hour

Quality quality = new Quality();

quality.name = Quality.ACCURACY;

quality.value = 0.95; // minimum value

ArrayList nfps = new ArrayList();

nfps.add(quality); nfps.add(costModel);

//find solvers met quality and cost needs

SBS elasticSolverUnit = (SBS)Relationship.elasticity(solverUnit2,nfps);

Object solverResult2 = elasticSolverUnit.execute("solving", params1);

}

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Excerpt – Programming location

dependency

//create a new software unit for simulation solvers

SBS solverUnit = new SBS("solver");

//make sure the solver unit and the preprocessing unit are in the same

data center

Relationship.locationDependency(preprocessingUnit,solverUnit,"Amaz

onEC2:Europe");

ArrayList params1 = new ArrayList();

params1.add(preProcessedData);

//execute solver unit with input data from preprocessing unit

Object solverResult= solverUnit.execute("solving",params1);

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Excerpt – Forwarding and

delegating analysis request

ICU dataScientist = new ICU();

//....

ICU fUnit = new ICU();

Relationship.socialrelation(dataScientist,

fUnit,1,"Linkedin:DataScienceGroup/TUWien");

Relationship.forward(data, fUnit);

//...

SCU studentSCU = new SCU();

//..

Relationship.delegate(data, studentSCU);

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Conclusions and Future Work

Hybrid compute units for implementing hybridity

and diversity-aware collective adaptive systems

(HDA-CAS)

Design fundamental programming elements and

constructs

We are still just at the abstract programming

elements/constructs

Need to work more on mapping from language

construct abstractions to runtime APIs

Runtime system working with several underlying

frameworks for supporting different composition and

coordination techniques

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Many thanks!

truong@dsg.tuwien.ac.at

http://dsg.tuwien.ac.at/research/viecom

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