Elastic Cognitive SystemsCognitive Systems Institute Group, 18 June 2015

Schahram Dustdar

Distributed Systems Group

TU Vienna

dsg.tuwien.ac.at

eHealth &

Smart Health

networksGame Machine

Telephone

PC

DVD

Audio

TV

STBDVC

Smart

Homes

Smart eGovernments &

eAdministrationsSmart Energy

Networks

Smart Evolution – People, Services,Things

Elastic Systems

Smart Transport

Networks

Marine Ecosystem: http://www.xbordercurrents.co.uk/wildlife/marine-ecosystem-2

Think Ecosystems:

People, Services, Things

Diverse users with complex networked dependencies and intrinsic adaptive behavior – has:

1. Robustness mechanisms: achieving stability in the presence of disruption

2. Measures of health: diversity, population trends, other key indicators

Approach

ElasticComputing

People

ThingsSoftware

Elastic Computing for the

Internet of Things

Smart City Dubai

Pacific Controls

Command Control Center

Connecting machines and people

Event Analyzer on

PaaS

Peak Operation

Other stakeholders

...

events stream

Normal Operation

Human Analysts

Peak OperationNormal Operation

Machine/Human

Event Analyzers

Critical

situation 1

Experts

SCU

(Big) Data analytics

Wf. A

Wf. B

Critical

situation 2

Cloud DaaS

Data analytics

M2M PaaS

Cloud IaaS

Operation

problem

Maintenance

process

Core principles:

Human computation capabilities under elastic service units

Augmenting human-based units together with software-based units

Elasticity ≠ Scaleability

Resource elasticity Software / human-based

computing elements,

multiple clouds

Quality elasticityNon-functional parameters e.g.,

performance, quality of data,

service availability, human

trust

Costs & Benefit

elasticityrewards, incentives

Elasticity

Specifying and controling elasticity

Basic primitives

Schahram Dustdar, Yike Guo, Rui Han,

Benjamin Satzger, Hong Linh Truong:

Programming Directives for Elastic Computing.

IEEE Internet Computing 16(6): 72-77 (2012)

SYBL (Simple Yet Beautiful Language) for

specifying elasticity requirements

SYBL-supported requirement levels

Cloud Service Level

Service Topology Level

Service Unit Level

Relationship Level

Programming/Code Level

Current SYBL implementation

in Java using Java annotations

@SYBLAnnotation(monitoring=„“,constraints=„“,strategies=„

“)

in XML

<ProgrammingDirective><Constraints><Constraint

name=c1>...</Constraint></Constraints>...</Programm

ingDirective>

as TOSCA Policies

<tosca:ServiceTemplate name="PilotCloudService">

<tosca:Policy name="St1"

policyType="SYBLStrategy"> St1:STRATEGY

minimize(Cost) WHEN high(overallQuality)

</tosca:Policy>...

Elasticity Model for Cloud ServicesMoldovan D., G. Copil,Truong H.-L., Dustdar S. (2013). MELA:

Monitoring and Analyzing Elasticity of Cloud Service. CloudCom

2013

Elasticity space functions: to determine if a

service unit/service is in the “elasticity behavior”

Elasticity Pathway functions: to characterize the

elasticity behavior from a general/particular view

Elasticity Space

Elastic Computing for

People

Human-based service elasticity

Which types of human-based service instances

can we provision?

How to provision these instances?

How to utilize these instances for different types

of tasks?

Can we program these human-based services

together with software-based services

How to program incentive strategies for human

services?

Computing Models

Machine-based

Computing

Human-based

Computing

Things-based

computing

Grid

Pro

cessin

g

Unit

Arc

hitectu

reC

om

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

Specifying and controling elasticity

of human-based services

What if we need to

invoke a human?

#predictive maintanance analyzing chiller measurement

#SYBL.ServiceUnitLevel

Mon1 MONITORING accuracy = Quality.Accuracy

Cons1 CONSTRAINT accuracy < 0.7

Str1 STRATEGY CASE Violated(Cons1):

Notify(Incident.DEFAULT, ServiceUnitType.HBS)

Evolution of Human-Based Computing

• Tim Berners-Lee’s Social Machines: “a computational entity that blends computational and social processes”

• Our view:• People AND Computational Units• Complex Workflows• Respond to ad-hoc situations• Leverage human creativity• Embrace uncertainty• No over-regulation• Human-driven adaptation

Complex collaborative patterns/workflows On-demand (machine-driven)

Open-call (human-driven)

Crowd satisfaction and non-monetary motivation Incentives and rewards

Reputation, accountability

Career ladders, reputation transfer, virtual careers, hierarchy

Developments in Human-Based Comp.

①

②

③

On-demand Collaborative Use-case

Hybrid ad-hoc collectives

Provisioned on-demand by the platform (e.g., SmartSociety, SCU)

①

Elastic SCU provisioning

Elastic profileSCU (pre-)runtime/static formation

Cloud APIs

Muhammad Z.C. Candra, Hong-Linh Truong, and Schahram

Dustdar, Provisioning Quality-aware Social Compute Units in

the Cloud, ICSOC 2013.

Algorithms

Ant Colony

Optimization

variants

FCFS

Greedy

SCU

extension/reduction

Task reassignment

based on trust, cost,

availability

Mirela Riveni, Hong-Linh Truong, and Schahram

Dustdar, On the Elasticity of Social Compute Units,

CAISE 2014

①

Motivating Scenario #2:

Human-driven: Collaborative Ride-Sharing

②

Open-call Collaborative Use-case

The platform composes the possible/optimal execution plans based on

subtask offers submitted by crowd members.

Plans are recommended/offered to interested crowd members

Crowd members are able to negotiate for participating in execution of

multiple plans concurrently, effectively making only a subset of them

happen.

Negotiation orchestrated by the platform

Composition

Recommendation

NegotiationExecution

Feedback

②

request

SmartSociety Platform

M. Rovatsos et al., Agent protocols for social computation Quality-aware Social Compute Units,

in Metaheuristics for Smart Cities, 2015.

P. Zeppezauer et al., Virtualizing communication for hybrid and diversity-aware collective adaptive

systems, WESOA@ICSOC, 2014.

http://www.smart-society-project.eu/publications/deliverables/D_6_1/

users

crowd of human andmachine peers

②

Crowd satisfaction and

non-monetary motivation

[2] Kittur, A., et al.: The future of crowd work. Proc. of CSCW ’13, New York, USA.

• How to make virtual labor market competitive and attractive for skilled workers? [2]

Complex collaborative patterns/workflows Hierarchy/structure Worker satisfaction and non-monetary motivation Reputation, accountability Career ladders, reputation transfer, virtual careers

Incentive Management

③

Operational Context③

Automated Incentive Management

abstraction

interlayer

③

Research Questions

abstraction

interlayer

• Identify common incentivizing patterns in existing systems• Express the patterns as compositions of fundamental,

platform-agnostic incentive elements.

③

Modeling Incentives

Examined incentive strategies in over 200 existing social computing platforms

Examined incentive mechanisms in economics, management science, sociology, psychology

Identified fundamental incentive mechanisms in use today and their constituent elements

New mechanisms can be built by composing and customizing well-known incentive elements

[3] Scekic, O., Truong, H.-L., Dustdar, S.: Incentives and rewarding in social computing. Communications of the ACM, 56(6), 72 (2013).

③

Research Questions

abstraction

interlayer

• Virtualize system-specific worker team representations into a system-agnostic model amenable to the application of incentives.

• Develop primitives for executing (applying) incentive actions.

③

Abstraction Interlayer

PRINC (PRogrammable INcentives) framework.

Allows modeling of human worker teams– storing and altering worker metrics

– storing and altering worker structure

– storing behavioral history and scheduling of incentive actions

Event-based communication with underlying socio-technical system

[4] Scekic, O., Truong, H.-L., Dustdar, S.: Modeling rewards and incentive mechanisms for social BPM. Proc. BPM’12 (pp. 150–155), Talinn, (2013).

[5] Scekic, O., Truong, H.-L., Dustdar, S.: Programming incentives in information systems. In Proc. CAiSE’13 (pp. 688–703), Valencia (2013).

③

Research Questions

abstraction

interlayer

• Design a declarative, human-friendly way of modeling incentives out of fundamental incentive elements.

• Translate the modeled incentive strategy into executable actions.

③

A Domain-Specific Language for

Incentives

[6] Scekic, O., Truong, H.-L., Dustdar, S.: Managing Incentives in Social Computing Systems with PRINGL. WISE’14 (pp. 415—424), Thessaloniki, Greece

PRINGL – PRogrammable INcentive Graphical Language

Visuo-textual language– Graphical elements for modeling and

composing incentive elements

– Traditional code snippets for additionalbusiness logic

System-independent

Human-friendly syntax

Elements can be stored, shared, reused

Translated to code executable on abstraction interlayer

③

Managing Incentives with PRINGL

Conclusions

Elastic Cognitive Computing is the next step in the man & machine „symbiosis“

Novel environments needed for

Complex collaboration patterns-> include Things, Services

Incentive management

Reputation transfer

Virtual careers

Thanks for your attention!

Prof. Dr. Schahram Dustdar

Distributed Systems GroupTU Wien

dsg.tuwien.ac.at