Building Scalable VirtualCommunities

Omer F. Rana(o.f.rana@cs.cardiff.ac.uk)

School of Computer Scienceand Welsh eScience Centre

Cardiff University

http://www.cs.cf.ac.uk/http://www.wesc.ac.uk/

Projects at WeSC

EPSRC (PASOA)EPSRC (GENSS)EU (Provenance)EU (CatNets)

EPSRCAgentcitiesUK.net

EU CoreGrid

EU NUMAS (?)EPSRC/DTI(CONNOISE)

DTI(GECEM)

G-QoSM

Talk in One Slide

eScience

Grid Computing

Models

Peer-2-PeerModels

Communities

Workflow

CommunityManagement

Dynamic Communities

Observations Outcomes

Integrating Workflow withCommunityFormation andManagement

TrianaLEAFG-QoSM

What are we “really” attempting in eScience?

• “Virtually” bringing together scientists to solve complex problems – which may often be multidisciplinary – Establishing Virtual Organisations– “Managed Complexity” not Ease-of-use?

• Dynamic formation of such collaborations (and their subsequent disbanding)– UKRC, EU, NSF, etc funding model– The Café/Restaurant/Pub model

• Mediating support between these individuals through compute, data and visualisation resources– Resource/service challenge is secondary?– Are we really understanding the “processes”?– Where is the novelty?

• Have we abandoned the initial motivation?– Power Grid may be a bad analogy?– Can we find others? Workflows +

Virtual Organisations Communities

Outcome of eScience?

• Software should be accessible outside the Grid Community

• Compare:– KaZaA (file sharing)

and BitTorrent (file sharing)

• Software should be useful outside the Grid community?

• Integrate different aspects of a collaboration– Nothing yet?– No Science focus yet

•Significant User Community •Supports DSL-modem users and high bandwidth users at the same time•Support a “Rating” mechanism

Variety of other types of groupings(social networking):

•YahooGroups•LinkedIn•Orkut (Google) •BuddySpace, etc

Gnutella Network -- Steve G. Steinberg).

Internet Social Networking (April 2004)

Source: TrendIQ

Also see: http://www.gridblog.com/Uniyearbooks.com (Dan Crocker, Cardiff)

Internet Social Networking (April/May 2004)

Source: TrendIQ

What does this tell us?

• Importance of “small world” interactions – Some participants more active than

others• Need to identify “hub” points for traffic

sharing– Co-authorship networks– File sharing networks

• Identify resource requirements of highly active participants

Sounds like the Grid?

“The key issues behind the Friendster abandonment trend, according to users, are the service's inability to do anything about its habitual server lag problems, and its growing reputation for heavy-handed moral policies and unilateral decisions it makes on behalf of its members.”

Wired News (wired.com)http://www.wired.com/news/culture/0,1284,61150,00.html

Workflow InstanceWorkflow InstanceWorkflow Instance

Workflow (+ Enactment)

Resource layer1000s of PCs ->massive supercomputers

and data sources

Information/NamingServices

Information/NamingServices

(co-)schedulingService

(co-)schedulingService

AccountingService

AccountingService

SecurityService

SecurityService

Event/MesgService

Event/MesgService

Discoveryservice

Discoveryservice

User HelpServices

User HelpServices

MonitoringService

MonitoringService

Peer Creation& resolution

Services

Peer Creation& resolution

Services

InformationRouting

InformationRouting

OGSA / Web ServicesApplication Services Layer

User Portals/ Science Portals

Launch, configureAnd control Orchestration Service

Workflow Engine

From:

Aleksander Slominski

Network

Problems with “Predictability”

Scientific Workflows• What makes it different (how

it is applied)?– Support for large data

flows – Need to do parameterized

execution of large number of jobs

– Need to monitor and control workflow execution including ad-hoc changes

– Need to execute in dynamic environment where resources are not know a priori and may need to adapt to changes

– Hierarchical execution with sub-workflows created and destroyed when necessary

• Science Domain specific requirements.

Workflow World

• Triana• Taverna/SCUFL• GridAnt• Condor DAG• CoG DAG• SWFL• BioOpera• BEPL4WS• OASIS WSBPEL• YAWL• GSFL• … etc

Origin (?):Problem Solving Environments(MatLab, Mathematica, SciRun,

NetSolve, Ninf, Nimrod etc)

Distributed Workflow Community

Service Provider

Manager

Registry

WF Executor

Community

Performance Info.

Workflows Communities• Communities may be:

– functional – based on service(s) offered, applications supported

– attribute – performance, security policy, trust, etc

• Community structure influenced by– capability of service providers– overall objectives/goals of community as a whole– “utility” (measurable) of a provider within a community

• Co-operative participants– coalition or team

• Competing participants – markets

Community: “logical” organisation, consisting of a set of service providers andusers working towards some common objectives, or sharing some common sets of “beliefs”

Related Efforts • Community Formation

– Learning based approaches -- co-operative agents (Sun et al.)

– Market and game theoretic approaches (Wellman et al.)

– Pursuit of common goals (Singh, Rao, Georgeff et al.)

– Joint intentions pursuit -- team oriented programming (Jennings et al., Tambe et al.)

– Shared Plans (Grosz and Kraus)– Coalitions (Sandholm, Lesser et al.)– Congregation Formation (Durfee, Vidal, Brooks

et al.)Other work in Sociology and Economics

Community Structure

• Service Providers and Users– provide or use application-specific services– providers possess “Expertise” and “Interests”

• Community Support Services – registry service – event service, monitoring service, etc

• Community Manager – community policy and goals– manages and monitors Service Level

Agreements – access control for new providers/users

Community, a useful idea?• Connection Problem: Finding suitable partners to

interact with • Intentions so far have been different

– problem solving capability preferred• Community Stability an important concern

– dependent on environment (and its variability)

• What impact does community formation have on scalability? – Reduce time to search for partners– Reduce time to co-ordinate (reduce message

exchanges)

Types of Communities

• Competing Communities (identical “Expertise”)– participants offer similar services– competition between providers

• Cooperative Communities (different “Expertise”)– participants need services of other providers to function– mutual dependence between providers

• Goal Oriented Communities (based on “Interests”)– similar to cooperative community– differs in that goal may change over time, but Expertise

may not– new providers allowed only if they posses the right

“Expertise” to achieve goal • Ad Hoc Communities (based on “Interests”)

– Short term interactions between providers and users • Domain-Oriented Communities (based on “Interests”)

– Interaction based on “Interests” – music sharing community, open-source community, etc

Utility

• Payoff function– assess behaviour of a particular action

(reward signal) • Analysis tool

– relationship between local utility vs. utility of the community

• Cost function– success w.r.t. a particular task

• Trust measure– measure of trust in a particular

participant

Utility Optimisation

Expected Utility – E(x)

Infinite Horizon

Finite Horizon

0<<1

“U” may be negative

Long term rewards less useful

Community Building Kit: LEAFFour core concepts:

LEAF agentsLEAF utility functionsESNsLEAF tasks

Provides support for:JESS based policy descriptionReinforcement learning

Assigning Utility in LEAF 

Performance and Functional

Utility

P

F

Speed of execution, number of tasks, CPU usage etc. Decision making,

learning -- high level behaviour.

Performance Utility• Provides a utility measure based on performance

engineering related aspects

– Comms metrics:• number of messages exchanged, size of

message, response time– Execution metrics:

• execution time, time to convergence, queue time

– Memory and I/O metrics: • Memory access time, disk access time

• The effect of implementation decisions (algorithms; languages) and deployment decisions (platforms; networks), can be assessed.

Functional Utility

• Utility based on “problem solving” capability

• Statically defined– related to service properties (capability based)– degree of match between task properties and

service capability• syntax match (exact match)• range match• semantic match (subsumption/subclass)

• Dynamically defined– related to execution output (MSE)

Utility Function Implementation

• Extend the LocalUtilityFunction abstract class.

• Implement the compute() method.

• Functions have access to remote parameters and observable properties.

• Coordination: utility functions are assigned to agents by an Environment Service Node.

LEAF: Learning Agent FIPA-Compliant Community Toolkit 

ESN

Community

f1

f2

LEAF: Learning Agent FIPA-Compliant Community Toolkit 

ESN

Community a

f1

f2

ESN

f3

Community b

sum f2,f3

Multiple utility functions can be assigned

LEAF: Learning Agent FIPA-Compliant Community Toolkit 

• Utility functions can have parameters that are not available locally to the agent.

LEAF: Learning Agent FIPA-Compliant Community Toolkit 

ESN

Community

R

O

O: observable propertiesR: remote parameters

f1

Access to utility functions

double computeFunctionalUtility()Computes the sum of all currently assigned functional utility functions.

  double computePerformanceUtility()Computes the sum of all currently assigned performance utility functions.

  String[] getFunctionalUtilityRequiredProperties()Returns the observable properties required to compute the agent’s functional utility functions.

  String[] getPerformanceUtilityRequiredProperties()Returns the observable properties required to compute the agent’s performance utility functions.

Others:UpdateTimeperiod, removePerformanceUtilityFunction, ESNConnect       

Via Community Manager

Service Provider

Manager

Registry

WF Executor

Community

Performance Info.

Simulation: Four types of Communities

• Task categories: Compute, Visualisation, Instrumentation and Storage

• Mainly performance utility evaluated

• Utility: rate of job completions (of those submitted, how many are completed) – Need cooperation (process locally or

send to another provider)

Utility Functions

where Tr is the number of tasks processed by resource provider r, and idler is the total time spent idle by the provider. c1,c2 are constants

where Aa is the number of applications processed by user a, and Ja is the total resource usage time used by a. c1,c2 are constants

Global Utility (G) = i Local Utility (Ui)

U = (jobs of type X processed)/(jobs of type X submitted)U = 1/(idle time)

0

5

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0 50 100 150 200 250time

Global UtilityNumber of Memberscomputational

community

Uti

lity

0

5

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0 50 100 150 200 250 300 350 400 450 500time

Global UtilityNumber of Membersstorage

community

Uti

lity

Co-operative Community

“Maybe we should writethat spot down.”

How to kill a (Grid) Mammoth

More Info.

• Triana– http://www.trianacode.org/– http://www.gridlab.org/– http://www.gridoned.org/

• LEAF– http://www.cs.cf.ac.uk/User/S.J.Lynden/leaf/

• G-QoSM– http://www.cs.cf.ac.uk/User/Rashid/ – http://www.wesc.ac.uk/projects/uddie/

Thanks to:

• Steven Lynden, Newcastle University• Ian Taylor, Matt Shields, Ian Wang, Ali

Shaikhali, Shalil Majithia, Asif Akram, Rashid Al-Ali and David W. Walker, Cardiff University

• Luc Moreau, Southampton University• Kaizar Amin and Gregor von Laszewski,

Argonne National Lab• Jose Cunha, Fernanda Barbosa and Cecilia

Gomes, New University of Lisbon